U.S. patent application number 16/112242 was filed with the patent office on 2019-02-28 for methods of disease activity profiling for personalized therapy management.
This patent application is currently assigned to Nestec S.A.. The applicant listed for this patent is Nestec S.A.. Invention is credited to Scott Hauenstein, Nicholas Hoe, Steve Lockton, Linda Ohrmund, Sharat Singh.
Application Number | 20190060449 16/112242 |
Document ID | / |
Family ID | 46147068 |
Filed Date | 2019-02-28 |
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United States Patent
Application |
20190060449 |
Kind Code |
A1 |
Singh; Sharat ; et
al. |
February 28, 2019 |
METHODS OF DISEASE ACTIVITY PROFILING FOR PERSONALIZED THERAPY
MANAGEMENT
Abstract
The present invention provides methods for personalized
therapeutic management of a disease in order to optimize therapy
and/or monitor therapeutic efficacy. In particular, the present
invention comprises measuring an array of one or a plurality of
biomarkers at a plurality of time points over the course of therapy
with a therapeutic agent to determine a mucosal healing index for
selecting therapy, optimizing therapy, reducing toxicity, and/or
monitoring the efficacy of therapeutic treatment. In certain
instances, the therapeutic agent is a TNF.alpha. inhibitor for the
treatment of a TNF.alpha.-mediated disease or disorder.
Inventors: |
Singh; Sharat; (Rancho Santa
Fe, CA) ; Hoe; Nicholas; (San Diego, CA) ;
Lockton; Steve; (San Diego, CA) ; Hauenstein;
Scott; (San Diego, CA) ; Ohrmund; Linda; (San
Diego, CA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Nestec S.A. |
Vevey |
|
CH |
|
|
Assignee: |
Nestec S.A.
Vevey
CH
|
Family ID: |
46147068 |
Appl. No.: |
16/112242 |
Filed: |
August 24, 2018 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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14072746 |
Nov 5, 2013 |
10086072 |
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16112242 |
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PCT/US2012/037375 |
May 10, 2012 |
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14072746 |
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61484607 |
May 10, 2011 |
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61505026 |
Jul 6, 2011 |
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61553909 |
Oct 31, 2011 |
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61566509 |
Dec 2, 2011 |
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61636575 |
Apr 20, 2012 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G01N 33/6893 20130101;
C12Q 1/6883 20130101; G01N 2800/065 20130101; C12Q 2600/158
20130101; C12Q 2600/156 20130101; C12Q 2600/106 20130101; A61K
39/3955 20130101 |
International
Class: |
A61K 39/395 20060101
A61K039/395; G01N 33/68 20060101 G01N033/68; C12Q 1/6883 20060101
C12Q001/6883 |
Claims
1. A non-invasive method for improving endoscopic monitoring of
mucosal healing in an individual receiving therapy having Crohn's
disease, the method comprising: (a) measuring the levels of an
array of mucosal healing markers in a sample from the individual at
a plurality of time points over the course of therapy; (b) applying
a statistical algorithm to the level of the one or more markers
determined in step (a) to generate a mucosal healing index; (c)
comparing the individual's mucosal healing index to that of a
control, wherein the control is an endoscopic score; and (d)
determining whether the therapy is appropriate for the individual
to promote mucosal healing.
2. The method of claim 1, wherein the therapy is a member selected
from the group consisting of TNF.alpha. inhibitor therapy, an
immunosuppressive agent, a corticosteroid, a drug that targets a
different mechanism, nutrition therapy, and combinations
thereof.
3. The method of claim 2, wherein the TNF.alpha. inhibitor therapy
comprises an anti-TNF.alpha. antibody.
4. The method of claim 3, wherein the anti-TNF.alpha. antibody is a
member selected from the group consisting of REMICADE.TM.
(infliximab), ENBREL.TM. (etanercept), HUMIRA.TM. (adalimumab),
CIMZIA.RTM. (certolizumab pegol), and combinations thereof.
5. The method of claim 2, wherein the immunosuppressive agent is a
member selected from the group consisting of azathioprine,
6-mercaptopurine, methotrexate, and combinations thereof.
6. The method of claim 2, wherein the drug that targets a different
mechanism is a member selected from the group consisting of an IL-6
receptor inhibiting antibody, an anti-integrin molecule, a JAK-2
inhibitor, a tyrosine kinase inhibitor, and combinations
thereof.
7. The method of claim 2, wherein the nutrition therapy comprises a
special carbohydrate diet.
8. The method of claim 1, wherein the markers are measured in a
sample selected from the group consisting of serum, plasma, whole
blood, stool, peripheral blood mononuclear cells (PBMC),
polymorphonuclear (PMN) cells, and a tissue biopsy.
9. The method of claim 1, wherein the mucosal healing marker is a
member selected from the group consisting of AREG, EREG, HB-EGF,
HGF, NRG1, NRG2, NRG3, NRG4, BTC, EGF, IGF, TGF-.alpha., VEGF-A,
VEGF-B, VEGF-C, VEGF-D, FGF1, FGF2, FGF7, FGF9, TWEAK and
combinations thereof.
10. The method of claim 1, wherein the array of mucosal healing
markers further comprises at least one member selected from the
group consisting of an anti-TNF.alpha. antibody, an anti-drug
antibody (ADA), an inflammatory marker, an anti-inflammatory
marker, a mucosal healing marker, and combinations thereof.
11. The method of claim 10, wherein the anti-TNF.alpha. antibody is
a member selected from the group consisting of REMICADE.TM.
(infliximab), ENBREL.TM. (etanercept), HUMIRA.TM. (adalimumab),
CIMZIA.RTM. (certolizumab pegol), and combinations thereof.
12. The method of claim 10, wherein the anti-drug antibody (ADA) is
a member selected from the group consisting of a human
anti-chimeric antibody (HACA), a human anti-humanized antibody
(HAHA), a human anti-mouse antibody (HAMA), and combinations
thereof.
13. The method of claim 10, wherein the inflammatory marker is a
member selected from the group consisting of GM-CSF, IFN-.gamma.,
IL-1.beta., IL-2, IL-6, IL-8, TNF-.alpha., sTNF RII, and
combinations thereof.
14. The method of claim 10, wherein the anti-inflammatory marker is
a member selected from the group consisting of IL-12p70, IL-10, and
combinations thereof.
15. The method of claim 1, wherein: (i) the marker is a member
selected from the group consisting of GM-CSF, IFN-.gamma.,
IL-1.beta., IL-2, IL-6, IL-8, TNF-.alpha., soluble tumor necrosis
factor-.alpha. receptor II (sTNF RII), TNF-related weak inducer of
apoptosis (TWEAK), osteoprotegerin (OPG), IFN-.alpha., IFN-.beta.,
IL-1.alpha., IL-1 receptor antagonist (IL-1ra), IL-4, IL-5, soluble
IL-6 receptor (sIL-6R), IL-7, IL-9, IL-12, IL-13, IL-15, IL-17,
IL-23, IL-27 and combinations thereof; or (ii) the marker is a
member selected from the group consisting of MMP-1, MMP-2, MMP-3,
MMP-7, MMP-8, MMP-9, MMP-12, MMP-13, MT1-MMP-1, and combinations
thereof; or (iii) the marker is a member selected from the group
consisting of C-reactive protein (CRP), D-dimer protein,
mannose-binding protein, alpha 1-antitrypsin, alpha
1-antichymotrypsin, alpha 2-macroglobulin, fibrinogen, prothrombin,
factor VIII, von Willebrand factor, plasminogen, complement
factors, ferritin, serum amyloid P component, serum amyloid A
(SAA), orosomucoid (alpha 1-acid glycoprotein (AGP)),
ceruloplasmin, haptoglobin, and combinations thereof; or (iv) the
marker is a member selected from the group consisting of
TGF-.alpha., TGF-.beta., TGF-.beta.2, and TGF-.beta.3 and
combinations thereof; or (v) the marker is a member selected from
the group consisting of AREG, EREG, HB-EGF, HGF, HRG, NRG1, NRG2,
NRG3, NRG4, BTC, EGF, IGF-1, TGF, VEGF-A, VEGF-B, VEGF-C, VEGF-D,
FGF1, FGF2, FGF7, FGF9, TWEAK and combinations thereof; or (vi) the
marker is a member selected from the group consisting of IL-10,
SCF, ICAM, VCAM, IL-12p40, VEGFA and combinations thereof.
16. The method of claim 1, wherein the marker is a member selected
from the group consisting of C-reactive protein (CRP), IL 7, MMP 1,
MMP 2, MMP 3, MMP 9, serum amyloid A (SAA), TGF.alpha., VCAM and a
combination thereof.
17. A method for monitoring mucosal healing in an individual having
Crohn's disease receiving therapy without the use of endoscopy, the
method comprising: (a) measuring the levels of an array of mucosal
healing markers in a sample from the individual at a plurality of
time points over the course of therapy; (b) applying a statistical
algorithm to the level of the one or more markers determined in
step (a) to generate a mucosal healing index; (c) comparing the
individual's mucosal healing index to that of a control, wherein
the control is an endoscopic score; and (d) determining whether the
therapy is appropriate for the individual to promote mucosal
healing.
18. The method of claim 17, wherein: (i) the marker is a member
selected from the group consisting of GM-CSF, IFN-.gamma.,
IL-1.beta., IL-2, IL-6, IL-8, TNF-.alpha., soluble tumor necrosis
factor-.alpha. receptor II (sTNF RII), TNF-related weak inducer of
apoptosis (TWEAK), osteoprotegerin (OPG), IFN-.alpha., IFN-.beta.,
IL-1.alpha., IL-1 receptor antagonist (IL-1ra), IL-4, IL-5, soluble
IL-6 receptor (sIL-6R), IL-7, IL-9, IL-12, IL-13, IL-15, IL-17,
IL-23, IL-27 and combinations thereof; or (ii) the marker is a
member selected from the group consisting of MMP-1, MMP-2, MMP-3,
MMP-7, MMP-8, MMP-9, MMP-12, MMP-13, MT1-MMP-1, and combinations
thereof; or (iii) the marker is a member selected from the group
consisting of C-reactive protein (CRP), D-dimer protein,
mannose-binding protein, alpha 1-antitrypsin, alpha
1-antichymotrypsin, alpha 2-macroglobulin, fibrinogen, prothrombin,
factor VIII, von Willebrand factor, plasminogen, complement
factors, ferritin, serum amyloid P component, serum amyloid A
(SAA), orosomucoid (alpha 1-acid glycoprotein (AGP)),
ceruloplasmin, haptoglobin, and combinations thereof; or (iv) the
marker is a member selected from the group consisting of
TGF-.alpha., TGF-.beta., TGF-.beta.2, TGF-.beta.3 and combinations
thereof; or (v) the marker is a member selected from the group
consisting of AREG, EREG, HB-EGF, HGF, HRG, NRG1, NRG2, NRG3, NRG4,
BTC, EGF, IGF-1, TGF, VEGF-A, VEGF-B, VEGF-C, VEGF-D, FGF1, FGF2,
FGF7, FGF9, TWEAK and combinations thereof; or (vi) the marker is a
member selected from the group consisting of IL-10, SCF, ICAM,
VCAM, IL-12p40, VEGFA and combinations thereof.
19. The method of claim 17, wherein the marker is a member selected
from the group consisting of C-reactive protein (CRP), IL 7, MMP 1,
MMP 2, MMP 3, MMP 9, serum amyloid A (SAA), TGF.alpha., VCAM and a
combination thereof.
20. The method of claim 17, wherein the therapy is a member
selected from the group consisting of TNF.alpha. inhibitor therapy,
an immunosuppressive agent, a corticosteroid, a drug that targets a
different mechanism, nutrition therapy, and combinations thereof.
Description
CROSS-REFERENCES TO RELATED APPLICATIONS
[0001] This application is a continuation of U.S. application Ser.
No. 14/072,746, filed Nov. 5, 2013, allowed, which application is a
continuation of Application No. PCT/US2012/037375, filed May 10,
2012, which application claims priority to U.S. Provisional Patent
Application No. 61/484,607, filed May 10, 2011, U.S. Provisional
Patent Application No. 61/505,026, filed Jul. 6, 2011, U.S.
Provisional Application No. 61/553,909, filed Oct. 31, 2011, U.S.
Provisional Application No. 61/566,509, filed Dec. 2, 2011, and
U.S. Provisional Application No. 61/636,575, filed Apr. 20, 2012,
the disclosures of which are hereby incorporated by reference in
their entirety for all purposes.
BACKGROUND OF THE INVENTION
[0002] Inflammatory bowel disease (IBD) which includes Crohn's
disease (CD) and ulverative colitis (UC) is a chronic idiopathic
inflammatory disorder affecting the gatrointestine tract. Disease
progression of CD and UC includes repeated episodes of inflammation
and ulceration of the intestine, leading to complications requiring
hospitalization, surgery and escalation of therapy (Peyrin-Biroulet
et al., Am. J. Gastroenterol, 105: 289-297 (2010); Langholz E.,
Dan. Med. Bull., 46: 400-415 (1999)). Current treatments such as
anti-tumor necrosis factor-alpha (TNF-.alpha.) biologics (e.g.,
infliximab (IFX), etanercept, adalimumab (ADL) and certolizumab
pegol), thiopurine drugs (e.g., azathioprine (AZA), 6-mercaptopurin
(6-MP)), anti-inflammatory drugs (e.g., mesalazine), and steroids
(e.g., corticosteroids) have been shown to reduce disease activity.
In some clinical trials of CD, mucosal healing which is described
as the absence of intestinal ulcers, was induced in patients on
combination therapy of corticosteroids, IFX and ADL. Furthermore,
MH was maintained in patients receiving IFX.
[0003] Other studies have shown that mucosal healing can be a
hallmark of suppression of bowel inflammation and predict long-term
disease remission (Froslie et al., Gastroenterology, 133: 412-422
(2007); Baert et al., Gastroenterology, (2010)). Long-term mucosal
healing has been associated with a decreased risk of colectomy and
colorectal cancer in UC patients, a decreased need for
corticosteroid treatment in CD patients, and possibly a decreased
need for hospitalization (Dave et al., Gastroenterology &
Hepatology, 8(1): 29-38 (2012)).
[0004] The International Organization for the Study of Inflammatory
Bowel Disease proposed defining mucosal healing in UC as the
absence of friability, blood, erosions an dulcers in all visualized
segments of gut mucosa (D'Haens et al,. Gastroenterology, 132:
763-786 (2007)). MH in CD was proposed to be the absence of ulcers.
The gold standard for measurement of Crohn's disease activity is
the Crohn's Disease Endoscopic Index of Severity (CDEIS). This
disease index score is established from several variables such as
superficial and deep ulceration, ulcerated and nonulcerated
stenosis, and surface area of ulcerated and disease segments. A
simplified version of the index is the Simple Endoscopic Score for
Crohn's Disease, which takes into account disease variables
including ulcer size, ulcerated surface, affected surface and
presence of narrowing. Both indices evaluate clinical symptoms of
CD, yet fail to measure the underlying cause of disease (e.g.,
inflammation) or resolution of disease (e.g., mucosal healing). A
measurement of mucosal healing can be performed to assess disease
induction as well as disease progression and resolution.
[0005] The process of mucosal healing begins with bleeding (e.g.,
degradation of the endothelial layers of the blood vessels) and
inflammation, then progresses to cell and tissue proliferation, and
finally tissue remodeling. At the inflammation stage, inflammatory
markers and anti-inflammatory markers, such as, but not limited to,
IL-1, IL-2, IL-6, IL-14, IL-17, TGF.beta., and TNF.alpha. are
expressed. During remodeling, tissue repair and remodeling growth
factors, such as, but not limited to, AREG, EREG, HB-EGF, HGF,
NRG1-4, BTC, EGF, IGF, TGF-.alpha., VEGFs, FGFs, and TWEAK are
expressed. Repair of the intestinal epithelium requires multiple
signal transduction pathways which are necessary for cell survival,
proliferation, and migration. We have identified novel markers of
mucosal healing that are predictive of the risk of disease relapse
and disease remission. A measurement of mucosal healing can be used
to periodically assess disease status in patients receiving a
therapy regimen.
[0006] Mucosal healing is typically assessed by endoscopy. Although
the invasive procedure is considered to be low-risk, its cost and
patient discomfort and compliance remain obstacles to frequent,
regular endoscopies to assess mucosal healing. There is an unmet
need in the art for non-invasive methods of determining mucosal
healing in a patient.
[0007] There is a need in the art for methods of therapeutic
management of diseases such as autoimmune disorders using an
individualized approach to optimize therapy and monitor efficacy.
The methods need to include assessing disease course and clinical
parameters such as phamacokinetics, disease activity indices,
disease burden, and mucosal status. The present invention satisfies
this need and provides related advantages as well.
BRIEF SUMMARY OF THE INVENTION
[0008] The present invention provides methods for personalized
therapeutic management of a disease in order to optimize therapy
and/or monitor therapeutic efficacy. In particular, the present
invention comprises measuring an array of one or a plurality of
mucosal healing biomarkers at one or a plurality of time points
over the course of therapy with a therapeutic agent to determine a
mucosal healing index for selecting therapy, optimizing therapy,
reducing toxicity, and/or monitoring the efficacy of therapeutic
treatment. In some embodiments, the therapy is an anti-TNF therapy,
an immunosuppressive agent, a corticosteroid, a drug that targets a
different mechanism, a nutrition therapy and combinations thereof.
In certain instances, the anti-TNF therapy is a TNF inhibitor
(e.g., anti-TNF drug, anti-TNF.alpha. antibody) for the treatment
of a TNF.alpha.-mediated disease or disorder.
[0009] TNF.alpha. has been implicated in inflammatory diseases,
autoimmune diseases, viral, bacterial and parasitic infections,
malignancies, and/or neurodegenerative diseases and is a useful
target for specific biological therapy in diseases, such as
rheumatoid arthritis and Crohn's disease. TNF inhibitors such as
anti-TNF.alpha. antibodies are an important class of therapeutics.
In some embodiments, the methods of the present invention
advantageously improve therapeutic management of patients with a
TNF.alpha.-mediated disease or disorder by optimizing therapy
and/or monitoring therapeutic efficacy to anti-TNF drugs such as
anti-TNF.alpha. therapeutic antibodies.
[0010] As such, in one aspect, the present invention provides a
non-invasive method for measuring mucosal healing in an individual
diagnosed with inflammatory bowel disease (IBD) receiving a therapy
regimen, the method comprising: [0011] (a) measuring the levels of
an array of mucosal healing markers in a sample from the
individual; [0012] (b) comparing the levels of an array of mucosal
healing markers in the individual to that of a control to compute
the mucosal healing index of the individual, wherein the mucosal
healing index comprises a representation of the extent of mucosal
healing; and [0013] (c) determining whether the individual
undergoing mucosal healing should maintain the therapy regimen.
[0014] As such, in one aspect, the present invention provides a
method for monitoring therapeutic efficiency in an individual with
IBD receiving therapy, the method comprising: [0015] (a) measuring
levels of an array of mucosal healing markers in a sample from the
individual at a plurality of time points over the course of therapy
with a therapeutic antibody; [0016] (b) applying a statistical
algorithm to the level of the one or more markers determined in
step (a) to generate a mucosal healing index; [0017] (c) comparing
the individual's mucosal healing index to that of a control; and
[0018] (d) determining whether the therapy is appropriate for the
individual to promote mucosal healing.
[0019] In another aspect, the present invention provides a method
for selecting a therapy regimen in an individual with IBD, the
method comprising: [0020] (a) measuring levels of an array of
mucosal healing markers in a sample from the individual at a
plurality of time points over the course of therapy, the individual
receiving a therapeutic antibody; [0021] (b) applying a statistical
algorithm to the level of the one or more markers determined in
step (a) to generate a mucosal healing index; [0022] (c) comparing
the individual's mucosal healing index to that of a control; and
[0023] (d) selecting an appropriate therapy regimen for the
individual wherein the therapy regimen promotes mucosal healing
[0024] As such, in another aspect, the present invention provides a
method for reducing or minimizing the risk of surgery in an
individual diagnosed with IBD being administered a therapy regimen,
the method comprising: [0025] (a) measuring an array of mucosal
healing markers at a plurality of time points over the course of
therapy with a therapeutic antibody; [0026] (b) generating the
individual's mucosal healing index comprising a representation of
the presence and/or concentration levels of each of the markers
over time; [0027] (c) comparing the individual's mucosal healing
index to that of a control, and [0028] (d) selecting an appropriate
therapy regimen for to reduce or minimize the risk of surgery.
[0029] As such, in another aspect, the present invention provides a
method for selecting a therapy regimen to promote mucosal healing
in an individual diagnosed with IBD, the method comprising: [0030]
(a) measuring levels of a panel of mucosal healing markers at time
point t.sub.0 to generate a mucosal healing index at to; [0031] (b)
measuring levels of a panel of mucosal healing markers at time
point t.sub.1 to generate a mucosal healing index at ti; [0032] (c)
comparing the change in the mucosal healing index from to to
t.sub.1; and [0033] (d) selecting the therapy regimen for the
individual to promote mucosal healing.
[0034] As such, in one aspect, the present invention provides a
non-invasive method for measuring mucosal healing in an individual
diagnosed with Crohn's disease receiving an anti-TNF therapy
regimen, the method comprising: [0035] (a) measuring the levels of
an array of mucosal healing markers in a sample from the
individual; [0036] (b) comparing the levels of an array of mucosal
healing markers in the individual to that of a control to compute
the mucosal healing index of the individual, wherein the mucosal
healing index comprises a representation of the extent of mucosal
healing; and [0037] (c) determining whether the individual
undergoing mucosal healing should maintain the anti-TNF therapy
regimen.
[0038] As such, in another aspect, the present invention provides a
method for monitoring therapeutic efficiency in an individual with
Crohn's disease receiving anti-TNF therapy, the method comprising:
[0039] (a) measuring levels of an array of mucosal healing markers
in a sample from the individual at a plurality of time points over
the course of therapy with a therapeutic antibody; [0040] (b)
applying a statistical algorithm to the level of the one or more
markers determined in step (a) to generate a mucosal healing index;
[0041] (c) comparing the individual's mucosal healing index to that
of a control; and [0042] (d) determining whether the anti-TNF
therapy is appropriate for the individual to promote mucosal
healing.
[0043] As such, in another aspect, the present invention provides a
method for selecting an anti-TNF therapy regimen in an individual
with Crohn's disease, the method comprising: [0044] (a) measuring
levels of an array of mucosal healing markers in a sample from the
individual at a plurality of time points over the course of
therapy, the individual receiving a therapeutic antibody; [0045]
(b) applying a statistical algorithm to the level of the one or
more markers determined in step (a) to generate a mucosal healing
index; [0046] (c) comparing the individual's mucosal healing index
to that of a control; and [0047] (d) selecting an appropriate
anti-TNF therapy regimen for the individual wherein the anti-TNF
therapy promotes mucosal healing.
[0048] As such, in another aspect, the present invention provides a
method for reducing or minimizing the risk of surgery in an
individual diagnosed with Crohn's disease being administered an
anti-TNF antibody therapy regimen, the method comprising: [0049]
(a) measuring an array of mucosal healing markers at a plurality of
time points over the course of therapy with a therapeutic antibody;
[0050] (b) generating the individual's mucosal healing index
comprising a representation of the presence and/or concentration
levels of each of the markers over time; [0051] (c) comparing the
individual's mucosal healing index to that of a control, and [0052]
(d) selecting an appropriate anti-TNF antibody therapy regimen for
to reduce or minimize the risk of surgery.
[0053] As such, in another aspect, the present invention provides a
method for selecting an anti-TNF antibody therapy regimen to
promote mucosal healing in an individual diagnosed with Crohn's
disease, the method comprising: [0054] (a) measuring levels of a
panel of mucosal healing markers at time point t.sub.0 to generate
a mucosal healing index at to; [0055] (b) measuring levels of a
panel of mucosal healing markers at time point t.sub.1 to generate
a mucosal healing index at ti; [0056] (c) comparing the change in
the mucosal healing index from t.sub.0 to t.sub.1; and [0057] (d)
selecting the anti-TNF antibody therapy regimen for the individual
to promote mucosal healing.
[0058] In some embodiments, the disease is a gastrointestinal
disease or an autoimmune disease. In certain instances, the subject
has Crohn's disease (CD) or rheumatoid arthritis (RA). In other
embodiments, the therapeutic antibody is an anti-TNF.alpha.
antibody. In some embodiments, the anti-TNF.alpha. antibody is a
member selected from the group consisting of REMICADE.TM.
(infliximab), ENBREL.TM. (etanercept), HUMIRA.TM. (adalimumab),
CIMZIA.RTM. (certolizumab pegol), and combinations thereof. In
preferred embodiments, the subject is a human.
[0059] In some embodiments, the array of markers comprises a
mucosal healing marker. In some embodiments, the mucosal marker
comprises AREG, EREG, HB-EGF, HGF, NRG1, NRG2, NRG3, NRG4, BTC,
EGF, IGF, TGF-.alpha., VEGF-A, VEGF-B, VEGF-C, VEGF-D, FGF1, FGF2,
FGF7, FGF9, TWEAK and combinations thereof
[0060] On other embodiments, the array of markers further comprises
a member selected from the group consisting of an anti-TNF.alpha.
antibody, an anti-drug antibody (ADA), an inflammatory marker, an
anti-inflammatory marker, a tissue repair marker (e.g., a growth
factor), and combinations thereof. In certain instances, the
anti-TNF.alpha. antibody is a member selected from the group
consisting of REMICADE.TM. (infliximab), ENBREL.TM. (etanercept),
HUMIRA.TM. (adalimumab), CIMZIA.RTM. (certolizumab pegol), and
combinations thereof. In certain other instances, the anti-drug
antibody (ADA) is a member selected from the group consisting of a
human anti-chimeric antibody (HACA), a human anti-humanized
antibody (HAHA), a human anti-mouse antibody (HAMA), and
combinations thereof. In yet other instances, the inflammatory
marker is a member selected from the group consisting of GM-CSF,
IFN-.gamma., IL-1(3, IL-2, IL-6, IL-8, TNF-.alpha., sTNF RII, and
combinations thereof. In further instances, the anti-inflammatory
marker is a member selected from the group consisting of IL-12p70,
IL-10, and combinations thereof.
[0061] In certain embodiments, the array comprises at least 2, 3,
4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21,
22, 23, 24, 25, 30, 35, 40, 45, 50, or more markers. In some
embodiments, the markers are measured in a biological sample
selected from the group consisting of serum, plasma, whole blood,
stool, peripheral blood mononuclear cells (PBMC), polymorphonuclear
(PMN) cells, and a tissue biopsy (e.g., from a site of inflammation
such as a portion of the gastrointestinal tract or synovial
tissue).
[0062] In certain embodiments, the plurality of time points
comprises at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15,
16, 17, 18, 19, 20, 25, 30, 35, 40, 45, 50, or more time points. In
some instances, the first time point in the plurality of time
points is prior to the course of therapy with the therapeutic
antibody. In other instances, the first time point in the plurality
of time points is during the course of therapy with the therapeutic
antibody. As non-limiting examples, each of the markers can be
measured prior to therapy with a therapeutic antibody and/or during
the course of therapy at one or more (e.g., a plurality) of the
following weeks: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15,
16, 17, 18, 19, 20, 22, 24, 26, 28, 30, 32, 34, 36, 38, 40, 42, 44,
46, 48, 50, 52, 54, 56, 58, 60, 62, 64, 66, 68, 70, 80, 90, 100,
etc.
[0063] In some embodiments, selecting an appropriate therapy
comprises maintaining, increasing, or decreasing a subsequent dose
of the course of therapy for the subject. In other embodiments, the
method further comprises determining a different course of therapy
for the subject. In certain instances, the different course of
therapy comprises treatment with a different anti-TNF.alpha.
antibody. In other instances, the different course of therapy
comprises the current course of therapy along with another
therapeutic agent, such as, but not limited to an anti-TNF therapy,
an immunosuppressive agent, a corticosteroid, a drug that targets a
different mechanism, a nutrition therapy and other combination
treatments.
[0064] In some embodiments, selecting an appropriate therapy
comprises selecting an appropriate therapy for initial treatment.
In some instances, the therapy comprises an anti-TNF.alpha.
antibody therapy.
[0065] In certain embodiments, the methods disclosed herein can be
used as confirmation that a proposed new drug or therapeutic is the
same as or is sufficiently similar to an approved drug product,
such that the proposed new drug can be used as a "biosimilar"
therapeutic. For example, if the proposed new drug has only a
slightly different disease activity profile compared to the branded
drug product, this would be apparent using the methods disclosed
herein. If the proposed new drug has a significantly different
disease activity profile compared to the branded drug product, then
the new drug would not be biosimilar. Advantageously, the methods
disclosed herein can be used in clinical trials of proposed new
drugs in order to assess the effective therapeutic efficacy or
value of the drug.
[0066] Accordingly, in some aspects, the methods of the invention
provide information useful for guiding treatment decisions for
patients receiving or about to receive anti-TNF drug therapy, e.g.,
by selecting an appropriate anti-TNF therapy for initial treatment,
by determining when or how to adjust or modify (e.g., increase or
decrease) the subsequent dose of an anti-TNF drug, by determining
when or how to combine an anti-TNF drug (e.g., at an initial,
increased, decreased, or same dose) with one or more
immunosuppressive agents such as methotrexate (MTX) or azathioprine
(AZA), and/or by determining when or how to change the current
course of therapy (e.g., switch to a different anti-TNF drug or to
a drug that targets a different mechanism such as an IL-6
receptor-inhibiting monoclonal antibody, anti-integrin molecule
(e.g., Tysabri, Vedaluzamab), JAK-2 inhibitor, and tyrosine kinase
inhibitor, or to a nutritition therapy (e.g., special carbohydrate
diet)).
[0067] In other embodiments, the methods of the present invention
can be used to predict responsiveness to a TNF.alpha. inhibitor,
especially to an anti-TNF.alpha. antibody in a subject having an
autoimmune disorder (e.g., rheumatoid arthritis, Crohn's Disease,
ulcerative colitis and the like.). In this method, by assaying the
subject for the correct or therapeutic dose of anti-TNF.alpha.
antibody, i.e., the therapeutic concentration level, it is possible
to predict whether the individual will be responsive to the
therapy.
[0068] In another embodiment, the present invention provides
methods for monitoring IBD (e.g., Crohn's disease and ulcerative
colitis) in a subject having the IBD disorder, wherein the method
comprises assaying the subject for the correct or therapeutic dose
of anti-TNF.alpha. antibody, i.e., the therapeutic concentration
level, over time. In this manner, it is possible to predict whether
the individual will be responsive to the therapy over the given
time period.
[0069] Other objects, features, and advantages of the present
invention will be apparent to one of skill in the art from the
following detailed description and figures.
BRIEF DESCRIPTION OF THE DRAWINGS
[0070] FIG. 1 shows a personalized IBD activity profile as
described in Example 1.
[0071] FIG. 2A show various patient infliximab concentrations as a
function of treatment time. FIG. 2B shows patient ranks over a
course of treatment with events (infliximab falling below a
threshold concentration) noted.
[0072] FIG. 3A show various patient HACA (ATI) concentrations as a
function of treatment time. FIG. 3B shows patient ranks over a
course of treatment with events (HACA detection or appearance)
noted.
[0073] FIG. 4A illustrates an association between the presence of
ATI and the level of IFX in patient samples. Samples with no
detectable level of ATI had a significantly higher IFX median
concentration, compared to sample with detectable ATI. FIG. 4B
illustrates that the presence of ATI correlates with higher CDAI.
FIG. 4C shows that concurrent immunosuppressant therapy (e.g., MTX)
is more likely to suppress the presence of ATI.
[0074] FIG. 5A shows that patients with ATI are more likely to
develop a poor response to treatment. FIG. 5B illustrates that the
inflammatory marker CRP is associated with increased levels of
ATI.
[0075] FIG. 6 illustrates that the protein levels of an array of
one or more inflammatory and tissue repair markers correlate to the
formation of antibodies to IFX.
[0076] FIG. 7A illustrates that an array of inflammatory markers
can be used to establish an inflammatory index what correlates with
the presence of ATI and/or disease progression. FIG. 7B shows the
relationship between the PII and IFX concentrations in samples with
ATI present. FIG. 7C illustrates that an exemplary PRO Inflammatory
Index correlates with levels of IFX (p<0.0001 and
R.sup.2=-0.129) in patient samples of the COMMIT study.
[0077] FIG. 8A illustrates the correlation between Crohn's Disease
Activity Index (CDAI) score and the concentration of infliximab in
serum in a number of patients in clinical study #1. FIG. 8B shows
that the presence of IFX in a sample correlated with a higher
CDAI.
[0078] FIG. 9A illustrates the association between IFX
concentration and the presence of antidrug antibodies to inflixamab
in samples analyzed. FIG. 9B illustrates that a high concentration
of ATI can lead to neutralizing antibodies and undetectable levels
of IFX. FIG. 9C illustrates that an ATI positive sample determined
at an early time point leads to a higher CDAI at a later time
point, compared to the lower CDAI level from an ATI negative
sample. "V1"=Visit 1; "V3"=Visit 3. FIG. 9D illustrates that in
clinical study #1, patients had lower odds of developing ATI if
receiving a combination therapy of infliximab and an
immunosuppressant agent (e.g., MTX and AZA).
[0079] FIG. 10A shows that correlation between IFX concentration
and the presence of ATI in samples of clinical study #2A. FIG. 10B
illustrates the relationship between ISA therapy and the presence
of ATI in the study. FIG. 10C illustrates the relationship between
CRP concentrations and the presence of ATI (ATI and/or neutralizing
ATI). FIG. 10D illustrates the relationship between loss of
responsiveness to IFX therapy and the presence of ATI in the
study.
[0080] FIG. 11 illustrates that levels of ATI and neutralizing
antibodies can be determined over time in a series of samples from
various patients.
[0081] FIG. 12A illustrates the comparison of CRP levels to the
presence of IFX. FIG. 12B illustrates the relationship between the
presence of ATI and the infusion reaction. FIG. 12C illustrates the
relationship between IFX concentration and the presence of ATI in
clinical study #2B. FIG. 12D illustrates the correlation between
the presence of ATI and the withdrawal of ISA therapy at a
specific, given date.
[0082] FIG. 13A illustrates the relationship between ATI and the
inflammatory marker CRP. Our analysis showed that the odds of
experiencing a loss of response to IFX was higher in patients
determined to be ATI positive at any time point. FIG. 13B
illustrates the correlation between the presence of ATI at any time
point and responsiveness to IFX treatment. FIG. 13C shows that loss
of response can be related to an increase in CRP. FIG. 13D
illustrates the association between the presence of IFX and CRP
levels.
[0083] FIG. 14A shows that lower IFX levels are associated with the
presence of ATI in clinical study #2C. FIG. 14B shows that lower
IFX levels are associated with the presence of ATI in clinical
study #3. FIG. 14C illustrates that the same correlation between
IFX levels and ATI was also present in the study data, follow-up
study and in the pharmacokinetics study.
[0084] FIG. 15A illustrates the relationship between ATI levels and
IFX. It was determined that samples with high concentration ATI are
neutralizing on IFX and thus, IFX concentration was determined to
be 0 .mu.g/ml. FIG. 15B illustrates an association between ADL
concentration and the presence of ATA in patient samples.
[0085] FIG. 16A describes the details of an exemplary PRO
Inflammatory Index. FIG. 16B illustrates that there is no obvious
relationship between the PII and the concentration of ADL in an
array of samples with ADL alone or in combination with other
drugs.
[0086] FIG. 17 shows a plot of the PII scores for patients
receiving Humira and Humira in combination with other drug such as
Remicade, Cimzia, Asathioprine and Methotrexate.
[0087] FIG. 18 shows details of methods for improved patient
management of CD and/or UC.
[0088] FIG. 19 shows the effect of the TNF-.alpha. pathway and
related pathways on different cell types, cellular mechanisms and
disease (e.g., Crohn's Disease (CD), rheumatoid arthritis (RA) and
Psoriasis (Ps)).
[0089] FIG. 20 illustrates an exemplary CEER multiplex growth
factor array.
[0090] FIGS. 21A-G illustrate multiplexed growth factor profiling
of patient samples using CEER growth factor arrays.
[0091] FIG. 22 illustrates the association between CRP levels and
the growth factor index score in determining disease remission.
[0092] FIGS. 23A-C illustrate embodiments of the present invention
to assist in developing personalized patient treatment for an IBD
patient with mild, moderate, or severe disease activity.
[0093] FIG. 24 illustrates a treatment paradigm to personalize
patient treatment. Monitoring of disease burden and mucosal healing
can assist in determining treatment selection, dose selection, and
initial drug response.
[0094] FIG. 25 shows the ROC analysis of CRP and IFX trough
thresholds.
[0095] FIGS. 26A-B show the relationship of CRP, serum IFX
concentration and ATI at sequential time points. FIG. 26A shows
presence of IFX and ATI in the pair's first data point and CRP in
the subsequent measurements. FIG. 26B shows CRP levels, IFX serum
concentration and ATI status at sequential time points for a
sample. In this sample CRP levels are lowest when the patient is
ATI- and has a serum IFX concentration higher than threshold.
[0096] FIG. 27 shows that there was no association between IFX
levels higher than threshold and CRP in ATI+ patients. Yet, in ATI-
patients CRP levels were significantly higher in patients with IFX
levels less than threshold (3 .mu.g/ml).
DETAILED DESCRIPTION OF THE INVENTION
I. INTRODUCTION
[0097] The present invention provides methods for measuring mucosal
healing in patients with IBD, CD and/or UC. In particular, the
present invention provides methods of measuring mucosal healing
markers wherein the markers are indicative of intestinal tissue
repair, and disease resolution or remission.
[0098] The present invention is advantageous because it addresses
and overcomes current limitations associated with monitoring
mucosal healing in patients with IBD (e.g., Crohn's disease and
ulcerative colitis). The present invention provides non-invasive
methods for monitoring mucosal healing patients receiving anti-TNF
therapy. In addition, the present invention provides methods of
predicting therapeutic response, risk of relapse, and risk of
surgery in patients with IBD (e.g., Crohn's disease and ulcerative
colitis). In particular, the methods of the present invention find
utility for selecting an appropriate anti-TNF therapy for initial
treatment, for determining when or how to adjust or modify (e.g.,
increase or decrease) the subsequent dose of an anti-TNF drug to
optimize therapeutic efficacy and/or to reduce toxicity, for
determining when or how to combine an anti-TNF drug (e.g., at an
initial, increased, decreased, or same dose) with one or more
immunosuppressive agents such as methotrexate (MTX) or azathioprine
(AZA), and/or for determining when or how to change the current
course of therapy (e.g., switch to a different anti-TNF drug or to
a drug that targets a different mechanism). The present invention
also provides methods for selecting an appropriate therapy for
patients diagnosed with CD, wherein the therapy promotes mucosal
healing.
II. DEFINITIONS
[0099] As used herein, the following terms have the meanings
ascribed to them unless specified otherwise.
[0100] The phrase "mucosal healing index" includes an empirically
derived index that is based upon an analysis of a plurality of
mucosal healing markers. In one aspect, the concentration of
markers or their measured concentration values are transformed into
an index by an algorithm resident on a computer. In certain
aspects, the index is a synthetic or human derived output, score,
or cut off value(s), which expresses the biological data in
numerical terms. The index can be used to determine or make or aid
in making a clinical decision. A mucosal healing index can be
measured multiple times over the course of time. In one aspect, the
algorithm can be trained with known samples and thereafter
validated with samples of known identity.
[0101] The phrase "mucosal healing index control" includes a
mucosal healing index derived from a healthy individual, or an
individual who has progressed from a disease state to a healthy
state. Alternatively, the control can be an index representing a
time course of a more diseased state to a less disease state or to
a healthy state.
[0102] The phrase "determining the course of therapy" and the like
includes the use of an empirically derived index, score or analysis
to select for example, selecting a dose of drug, selecting an
appropriate drug, or a course or length of therapy, a therapy
regimen, or maintenance of an existing drug or dose. In certain
aspects, a derived or measured index can be used to determine the
course of therapy.
[0103] The terms "TNF inhibitor", "TNF-.alpha. inhibitor" and
"TNF.alpha. inhibitor" as used herein are intended to encompass
agents including proteins, antibodies, antibody fragments, fusion
proteins (e.g., Ig fusion proteins or Fc fusion proteins),
multivalent binding proteins (e.g., DVD Ig), small molecule
TNF-.alpha. antagonists and similar naturally- or
nonnaturally-occurring molecules, and/or recombinant and/or
engineered forms thereof, that, directly or indirectly, inhibits
TNF .alpha. activity, such as by inhibiting interaction of
TNF-.alpha. with a cell surface receptor for TNF-.alpha.,
inhibiting TNF-.alpha. protein production, inhibiting TNF-.alpha.
gene expression, inhibiting TNF.alpha. secretion from cells,
inhibiting TNF-.alpha. receptor signaling or any other means
resulting in decreased TNF-.alpha. activity in a subject. The term
"TNF.alpha. inhibitor" preferably includes agents which interfere
with TNF-.alpha. activity. Examples of TNF-.alpha. inhibitors
include etanercept (ENBREL.TM., Amgen), infliximab (REMICADE.TM.,
Johnson and Johnson), human anti-TNF monoclonal antibody adalimumab
(D2E7/HUMIRA.TM., Abbott Laboratories), CDP 571 (Celltech), and CDP
870 (Celltech), as well as other compounds which inhibit
TNF-.alpha. activity, such that when administered to a subject
suffering from or at risk of suffering from a disorder in which
TNF-.alpha. activity is detrimental (e.g., RA), the disorder is
treated.
[0104] The term "predicting responsiveness to a TNF.alpha.
inhibitor", as used herein, is intended to refer to an ability to
assess the likelihood that treatment of a subject with a TNF
inhibitor will or will not be effective in (e.g., provide a
measurable benefit to) the subject. In particular, such an ability
to assess the likelihood that treatment will or will not be
effective typically is exercised after treatment has begun, and an
indicator of effectiveness (e.g., an indicator of measurable
benefit) has been observed in the subject. Particularly preferred
TNF.alpha. inhibitors are biologic agents that have been approved
by the FDA for use in humans in the treatment of rheumatoid
arthritis, which agents include adalimumab (HUMIRA.TM.), infliximab
(REMICADE.TM.) and etanercept (ENBREL.TM.), most preferably
adalimumab (HUMIRA.TM.).
[0105] The term "course of therapy" includes any therapeutic
approach taken to relieve or prevent one or more symptoms
associated with a TN.alpha.F-mediated disease or disorder. The term
encompasses administering any compound, drug, procedure, and/or
regimen useful for improving the health of an individual with a
TNF.alpha.-mediated disease or disorder and includes any of the
therapeutic agents described herein. One skilled in the art will
appreciate that either the course of therapy or the dose of the
current course of therapy can be changed (e.g., increased or
decreased) based upon the presence or concentration level of TNF,
anti-TNF drug, and/or anti-drug antibody using the methods of the
present invention.
[0106] The term "immunosuppressive agent" includes any substance
capable of producing an immunosuppressive effect, e.g., the
prevention or diminution of the immune response, as by irradiation
or by administration of drugs such as anti-metabolites,
anti-lymphocyte sera, antibodies, etc. Examples of suitable
immunosuppressive agents include, without limitation, thiopurine
drugs such as azathioprine (AZA) and metabolites thereof;
anti-metabolites such as methotrexate (MTX); sirolimus (rapamycin);
temsirolimus; everolimus; tacrolimus (FK-506); FK-778;
anti-lymphocyte globulin antibodies, anti-thymocyte globulin
antibodies, anti-CD3 antibodies, anti-CD4 antibodies, and
antibody-toxin conjugates; cyclosporine; mycophenolate; mizoribine
monophosphate; scoparone; glatiramer acetate; metabolites thereof;
pharmaceutically acceptable salts thereof; derivatives thereof;
prodrugs thereof; and combinations thereof.
[0107] The term "thiopurine drug" includes azathioprine (AZA),
6-mercaptopurine (6-MP), or any metabolite thereof that has
therapeutic efficacy and includes, without limitation,
6-thioguanine (6-TG), 6-methylmercaptopurine riboside,
6-thioinosine nucleotides (e.g., 6-thioinosine monophosphate,
6-thioinosine diphosphate, 6-thioinosine triphosphate),
6-thioguanine nucleotides (e.g., 6-thioguanosine monophosphate,
6-thioguanosine diphosphate, 6-thioguanosine triphosphate),
6-thioxanthosine nucleotides (e.g., 6-thioxanthosine monophosphate,
6-thioxanthosine diphosphate, 6-thioxanthosine triphosphate),
derivatives thereof, analogues thereof, and combinations
thereof.
[0108] The term "sample" as used herein includes any biological
specimen obtained from a patient. Samples include, without
limitation, whole blood, plasma, serum, red blood cells, white
blood cells (e.g., peripheral blood mononuclear cells (PBMC),
polymorphonuclear (PMN) cells), ductal lavage fluid, nipple
aspirate, lymph (e.g., disseminated tumor cells of the lymph node),
bone marrow aspirate, saliva, urine, stool (i.e., feces), sputum,
bronchial lavage fluid, tears, fine needle aspirate (e.g.,
harvested by random periareolar fine needle aspiration), any other
bodily fluid, a tissue sample such as a biopsy of a site of
inflammation (e.g., needle biopsy), and cellular extracts thereof.
In some embodiments, the sample is whole blood or a fractional
component thereof such as plasma, serum, or a cell pellet. In other
embodiments, the sample is obtained by isolating PBMCs and/or PMN
cells using any technique known in the art. In yet other
embodiments, the sample is a tissue biopsy, e.g., from a site of
inflammation such as a portion of the gastrointestinal tract or
synovial tissue.
[0109] The term "Crohn's Disease Activity Index" or "CDAI" includes
a research tool used to quantify the symptoms of patients with
Crohn's disease (CD). The CDAI is generally used to define response
or remission of CD. The CDAI consists of eight factors, each summed
after adjustment with a weighting factor. The components of the
CDAI and weighting factors are the following:
TABLE-US-00001 Weighting Clinical or laboratory variable factor
Number of liquid or soft stools each day for seven days .times.2
Abdominal pain (graded from 0-3 on severity) each .times.5 day for
seven days General well being, subjectively assessed from 0 (well)
.times.7 to 4 (terrible) each day for seven days Presence of
complications* .times.20 Taking Lomitil or opiates for diarrhea
.times.30 Presence of an abdominal mass (0 as none, .times.10 2 as
questionable, 5 as definite) Hematocrit of <0.47 in men and
<0.42 in women .times.6 Percentage deviation from standard
weight .times.1
One point each is added for each set of complications:
[0110] the presence of joint pains (arthralgia) or frank
arthritis;
[0111] inflammation of the iris or uveitis;
[0112] presence of erythema nodosum, pyoderma gangrenosum, or
aphthous ulcers;
[0113] anal fissures, fistulae or abscesses;
[0114] other fistulae; and/or
[0115] fever during the previous week.
[0116] Remission of Crohn's disease is typically defined as a fall
in the CDAI of less than 150 points. Severe disease is typically
defined as a value of greater than 450 points. In certain aspects,
response to a particular medication in a Crohn's disease patient is
defined as a fall of the CDAI of greater than 70 points.
[0117] The terms "mucosal injury" or "mucosal damage" include the
formation of macroscopically visible mucosal lesions in the
intestines detectable during endoscopy, granuloma formation and
disruption of the muscularis layer at the microscopic tissue level,
epithelial apoptosis and infiltration of activated inflammatory and
lymphocytic cells at the cellular level, increased epithelial
permeability at a sub-cellular level, and gap junction disruption
at a molecular level. In IBD such as Crohn's disease, the
intestinal epithelium is damaged by the inflammatory environment,
which results in the formation of refractory ulcers and
lesions.
[0118] The term "mucosal healing" refers to restoration of normal
mucosal appearance of a previously inflamed region, and complete
absence of ulceration and inflammation at the endoscopic and
microscopic levels. Mucosal healing includes repair and restoration
of the mucosa, submucosa, and muscularis layers. It can also
include neuronal and lymphangiogenic elements of the intestinal
wall.
[0119] The term "nutrition-based therapy " includes butyrate,
probiotics (e.g., VSL#3, E. coli Nissle 1917, bacterium bacillus
polyfermenticus), vitamins, proteins, macromolecules, and/or
chemicals that promote mucosal healing such as growth and turnover
of intestinal mucosa.
III. DESCRIPTION OF THE EMBODIMENTS
[0120] The present invention provides methods for personalized
therapeutic management of a disease in order to optimize therapy
and/or monitor therapeutic efficacy. In particular, the present
invention comprises measuring an array of one or a plurality of
mucosal healing biomarkers at one or a plurality of time points
over the course of therapy with a therapeutic agent to determine a
mucosal healing index for selecting therapy, optimizing therapy,
reducing toxicity, and/or monitoring the efficacy of therapeutic
treatment. In certain instances, the therapeutic agent is a
TNF.alpha. inhibitor for the treatment of a TNF.alpha.-mediated
disease or disorder. In some embodiments, the methods of the
present invention advantageously improve therapeutic management of
patients with a TNF.alpha.-mediated disease or disorder by
optimizing therapy and/or monitoring therapeutic efficacy to
anti-TNF drugs such as anti-TNF.alpha. therapeutic antibodies.
[0121] As such, in one aspect, the present invention provides a
method for personalized therapeutic management of a disease in
order to optimize therapy or monitor therapeutic efficacy in a
subject, the method comprising: [0122] (a) measuring an array of
mucosal healing markers at a plurality of time points over the
course of therapy with a therapeutic antibody; [0123] (b)
generating the subject's mucosal healing index comprising a
representation of the presence and/or concentration levels of each
of the markers over time; [0124] (c) comparing the subject's
mucosal healing index to that of a control; and [0125] (d)
selecting an appropriate therapy for the subject, to thereby
achieve personalized therapeutic management of the disease in the
subject.
[0126] As such, in another aspect, the present invention provides a
method for personalized therapeutic management of a disease in
order to select therapy in a subject, the method comprising: [0127]
(a) measuring an array of mucosal healing markers; [0128] (b)
generating the subject's mucosal healing index comprising a
representation of the presence and/or concentration levels of each
of the markers; [0129] (c) comparing the subject's mucosal healing
index to that of a control; and [0130] (d) selecting an appropriate
therapy for the subject, to thereby achieve personalized
therapeutic management of the disease in the subject.
[0131] As such, in one aspect, the present invention provides a
method for optimizing therapy in a subject, the method comprising:
[0132] (a) measuring an array of mucosal healing markers at a
plurality of time points over the course of therapy with a
therapeutic antibody; [0133] (b) applying a statistical algorithm
to the level of the one or more markers determined in step (a) to
generate a mucosal healing index; [0134] (c) comparing the
subject's mucosal healing index to that of a control; and [0135]
(d) determining a subsequence dose of the course of therapy for the
subject or whether a different course of therapy should be
administered to the subject based upon the mucosal healing
index.
[0136] As such, in one aspect, the present invention provides a
method for selecting therapy in a subject, the method comprising:
[0137] (a) measuring an array of mucosal healing markers at a
plurality of time points over the course of therapy with a
therapeutic antibody; [0138] (b) applying a statistical algorithm
to the level of the one or more markers determined in step (a) to
generate a mucosal healing index; [0139] (c) comparing the
subject's mucosal healing index to that of a control; and [0140]
(d) selecting an appropriate course of therapy for the subject
based upon the mucosal healing index.
[0141] As such, in another aspect, the present invention provides a
method for reducing the risk of surgery in a subject diagnosed with
IBD (e.g., Crohn's disease) being administered a therapy regimen
(e.g., an anti-TNF antibody therapy regimen), the method
comprising: [0142] (a) measuring an array of mucosal healing
markers at a plurality of time points over the course of therapy
with a therapeutic antibody; [0143] (b) applying a statistical
algorithm to the level of the one or more markers determined in
step (a) to generate a mucosal healing index; [0144] (c) comparing
the subject's mucosal healing index to that of a control; and
[0145] (d) determining whether the therapy regimen is reducing the
subject's risk of surgery.
[0146] As such, in one aspect, the present invention provides a
method for monitoring therapeutic efficiency in a subject receiving
therapy (e.g., anti-TNF therapy), the method comprising: [0147] (a)
measuring an array of mucosal healing markers at a plurality of
time points over the course of therapy with a therapeutic antibody;
[0148] (b) applying a statistical algorithm to the level of the one
or more markers determined in step (a) to generate a mucosal
healing index; [0149] (c) comparing the subject's mucosal healing
index to that of a control; and [0150] (d) determining whether the
current course of therapy is appropriate for the subject based upon
the mucosal healing index.
[0151] In some embodiments, the disease is a gastrointestinal
disease or an autoimmune disease. In certain instances, the subject
has inflammatory bowel disease (IBD, e.g., Crohn's disease (CD) or
ulcerative colitis (UC)). In other instances, the subject has
rheumatoid arthritis (RA). In preferred embodiments, the subject is
a human.
[0152] In some embodiments, the therapy is selected from the group
comprising an anti-TNF therapy, an immunosuppressive agent, a
corticosteroid, a drug that targets a different mechanism, a
nutrition therapy or combinations thereof. In certain instances,
the anti-TNF therapy is a TNF inhibitor (e.g., anti-TNF drug,
anti-TNF.alpha. antibody).
[0153] In other embodiments, the anti-TNF therapy is an
anti-TNF.alpha. antibody. In some embodiments, the anti-TNF.alpha.
antibody is a member selected from the group consisting of
REMICADE.TM. (infliximab), ENBREL.TM. (etanercept), HUMIRA.TM.
(adalimumab), CIMZIA.RTM. (certolizumab pegol), and combinations
thereof. In preferred embodiments, the subject is a human.
[0154] In some embodiments, the therapy is an immunosuppressive
agent. Non-limiting examples of immunosuppressive agents include
thiopurine drugs such as azathioprine (AZA), 6-mercaptopurine
(6-MP), and/or any metabolite thereof that has therapeutic efficacy
and includes, without limitation, 6-thioguanine (6-TG),
6-methylmercaptopurine riboside, 6-thioinosine nucleotides (e.g.,
6-thioinosine monophosphate, 6-thioinosine diphosphate,
6-thioinosine triphosphate), 6-thioguanine nucleotides (e.g.,
6-thioguanosine monophosphate, 6-thioguanosine diphosphate,
6-thioguanosine triphosphate), 6-thioxanthosine nucleotides (e.g.,
6-thioxanthosine monophosphate, 6-thioxanthosine diphosphate,
6-thioxanthosine triphosphate), derivatives thereof, analogues
thereof, and combinations thereof anti-metabolites such as
methotrexate (MTX); sirolimus (rapamycin); temsirolimus;
everolimus; tacrolimus (FK-506); FK-778; anti-lymphocyte globulin
antibodies, anti-thymocyte globulin antibodies, anti-CD3
antibodies, anti-CD4 antibodies, and antibody-toxin conjugates;
cyclosporine; mycophenolate; mizoribine monophosphate; scoparone;
glatiramer acetate; metabolites thereof; pharmaceutically
acceptable salts thereof; derivatives thereof; prodrugs thereof;
and combinations thereof.
[0155] In other embodiments, the therapy is a corticosteroid. In
yet other embodiments, the therapy is a drug that targets a
different mechanism (e.g., a mechanism that is not mediated by the
TNF.alpha. pathway). Non-limiting examples of a drug that targets a
different mechanism include IL-6 receptor inhibiting monoclonal
antibodies, anti-integrin molecules (e.g., natalizumab (Tysabri),
vedoluzamab), JAK-2 inhibitors, tyrosine kinase inhibitors, and
combinations thereof.
[0156] In other embodiments, the therapy is a nutrition therapy. In
particular embodiments, the nutrition therapy is a special
carbohydrate diet. Special carbohydrate diet (SCD) is a strict
grain-free, lactose-free, and sucrose-free nutritional regimen that
was designed to reduce the symptoms of IBD such as Crohn's disease
and ulcerative colitis. It has been shown that SCD can promote
and/or maintain mucosal healing in patients with IBD (e.g., Crohn's
disease or ulcerative colitis). Typically, SCD restricts the use of
complex carbohydrates and eliminates refined sugar, grains and
starch from the diet. It has been described that the microvilli of
patients with IBD lack the ability to break down specific types of
complex carbohydrates, resulting in the overgrowth of harmful
bacteria and irritation of the gut mucosa. It has been recommended
that SCD is a therapy for IBD (e.g., Crohn's disease or ulcerative
colitis) because it enables the gut to undergo mucosal healing.
[0157] In some embodiments, the array of markers comprises a
mucosal healing marker. In some embodiments, the mucosal marker
comprises AREG, EREG, HB-EGF, HGF, NRG1, NRG2, NRG3, NRG4, BTC,
EGF, IGF, TGF-.alpha., VEGF-A, VEGF-B, VEGF-C, VEGF-D, FGF1, FGF2,
FGF7, FGF9, TWEAK and combinations thereof.
[0158] In other embodiments, the array of markers further comprises
a member selected from the group consisting of an anti-TNF.alpha.
antibody, an anti-drug antibody (ADA), an inflammatory marker, an
anti-inflammatory marker, a tissue repair marker (e.g., a growth
factor), and combinations thereof In certain instances, the
anti-TNF.alpha. antibody is a member selected from the group
consisting of REMICADE.TM. (infliximab), ENBREL.TM. (etanercept),
HUMIRA.TM. (adalimumab), CIMZIA.RTM. (certolizumab pegol), and
combinations thereof. In certain other instances, the anti-drug
antibody (ADA) is a member selected from the group consisting of a
human anti-chimeric antibody (HACA), a human anti-humanized
antibody (HAHA), a human anti-mouse antibody (HAMA), and
combinations thereof. In yet other instances, the inflammatory
marker is a member selected from the group consisting of GM-CSF,
IFN-.gamma., IL-1.beta., IL-2, IL-6, IL-8, TNF-.alpha., sTNF RII,
and combinations thereof. In further instances, the
anti-inflammatory marker is a member selected from the group
consisting of IL-12p70, IL-10, and combinations thereof.
[0159] In certain embodiments, the array comprises at least 2, 3,
4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21,
22, 23, 24, 25, 30, 35, 40, 45, 50, or more markers. In some
embodiments, the markers are measured in a biological sample
selected from the group consisting of serum, plasma, whole blood,
stool, peripheral blood mononuclear cells (PBMC), polymorphonuclear
(PMN) cells, and a tissue biopsy (e.g., from a site of inflammation
such as a portion of the gastrointestinal tract or synovial
tissue).
[0160] In certain embodiments, the plurality of time points
comprises at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15,
16, 17, 18, 19, 20, 25, 30, 35, 40, 45, 50, or more time points. In
some instances, the first time point in the plurality of time
points is prior to the course of therapy with the therapeutic
antibody. In other instances, the first time point in the plurality
of time points is during the course of therapy with the therapeutic
antibody. As non-limiting examples, each of the markers can be
measured prior to therapy with a therapeutic antibody and/or during
the course of therapy at one or more (e.g., a plurality) of the
following weeks: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15,
16, 17, 18, 19, 20, 22, 24, 26, 28, 30, 32, 34, 36, 38, 40, 42, 44,
46, 48, 50, 52, 54, 56, 58, 60, 62, 64, 66, 68, 70, 80, 90, 100,
etc.
[0161] In further embodiments, the method for assessing or
measuring mucosal healing further comprises comparing the
determined level of the mucosal healing marker present in a sample
to an index value or cutoff value or reference value or threshold
value, wherein the level of the mucosal healing marker above or
below that value is predictive or indicative of an increased or
higher likelihood of the subject either undergoing mucosal healing
or not undergoing mucosal healing. One skilled in the art will
understand that the index value or cutoff value or reference value
or threshold value is in units such as mg/ml, .mu.g/ml, ng/ml,
pg/ml, fg/ml, EU/ml, or U/ml depending on the marker of interest
that is being measured.
[0162] In some embodiments, the mucosal healing index includes an
empirically derived index that is based upon an analysis of a
plurality of mucosal healing markers. In one aspect, the
concentration of markers or their measured concentration values are
transformed into an index by an algorithm resident on a computer.
In certain aspects, the index is a synthetic or human derived
output, score, or cut off value(s), which expresses the biological
data in numerical terms. The index can be used to determine or make
or aid in making a clinical decision. A mucosal healing index can
be measured multiple times over the course of time. In one aspect,
the algorithm can be trained with known samples and thereafter
validated with samples of known identity.
[0163] In some embodiments, the mucosal healing index control is a
mucosal healing index derived from a healthy individual, or an
individual who has progressed from a disease state to a healthy
state. Alternatively, the control can be an index representing a
time course of a more diseased state or healthy to disease.
[0164] In some embodiments, the methods of determining the course
of therapy and the like include the use of an empirically derived
index, score or analysis to select for example, selecting a dose of
drug, selecting an appropriate drug, or a course or length of
therapy, a therapy regimen, or maintenance of an existing drug or
dose. In certain aspects, a derived or measured index can be used
to determine the course of therapy.
[0165] In some embodiments, mucosal healing can be assessed or
monitored by endoscopy.
[0166] Non-limiting examples of endoscopy include video capsule
endoscopy (capsule endoscopy), disposable endoscopy, and 3D
endoscopy. In other embodiment, the mucosal healing index is
monitored or confirmed by endoscopy.
[0167] In some embodiments, selecting an appropriate therapy
comprises maintaining, increasing, or decreasing a subsequent dose
of the course of therapy for the subject. In other embodiments, the
method further comprises determining a different course of therapy
for the subject. In certain instances, the different course of
therapy comprises treatment with a different anti-TNF.alpha.
antibody. In other instances, the different course of therapy
comprises the current course of therapy along with another
therapeutic agent, such as, but not limited to, an
immunosuppressive agent, a corticosteroid, a drug that targets a
different mechanism, nutrition therapy, and combinations
thereof).
[0168] In some embodiments, selecting an appropriate therapy
comprises selecting an appropriate therapy for initial treatment.
In some instances, the therapy comprises an anti-TNF.alpha.
antibody therapy.
[0169] In certain embodiments, the methods disclosed herein can be
used as confirmation that a proposed new drug or therapeutic is the
same as or is sufficiently similar to an approved drug product,
such that the proposed new drug can be used as a "biosimilar"
therapeutic. For example, if the proposed new drug has only a
slightly different disease activity profile compared to the branded
drug product, this would be apparent using the methods disclosed
herein. If the proposed new drug has a significantly different
disease activity profile compared to the branded drug product, then
the new drug would not be biosimilar. Advantageously, the methods
disclosed herein can be used in clinical trials of proposed new
drugs in order to assess the effective therapeutic value of the
drug.
[0170] Accordingly, in some aspects, the methods of the invention
provide information useful for guiding treatment decisions for
patients receiving or about to receive anti-TNF drug therapy, e.g.,
by selecting an appropriate anti-TNF therapy for initial treatment,
by determining when or how to adjust or modify (e.g., increase or
decrease) the subsequent dose of an anti-TNF drug, by determining
when or how to combine an anti-TNF drug (e.g., at an initial,
increased, decreased, or same dose) with one or more
immunosuppressive agents such as methotrexate (MTX) or azathioprine
(AZA), and/or by determining when or how to change the current
course of therapy (e.g., switch to a different anti-TNF drug or to
a drug that targets a different mechanism such as an IL-6
receptor-inhibiting monoclonal antibody, anti-integrin molecule
(e.g., Tysabri, Vedaluzamab), JAK-2 inhibitor, and tyrosine kinase
inhibitor, or to a nutritition therapy (e.g., special carbohydrate
diet)).
[0171] In other embodiments, the methods of the present invention
can be used to predict responsiveness to a TNF.alpha. inhibitor,
especially to an anti-TNF.alpha. antibody in a subject having an
autoimmune disorder (e.g., rheumatoid arthritis, Crohn's Disease,
ulcerative colitis and the like.). In this method, by assaying the
subject for the correct or therapeutic dose of anti-TNF.alpha.
antibody, i.e., the therapeutic concentration level, it is possible
to predict whether the individual will be responsive to the
therapy.
[0172] In another embodiment, the present invention provides
methods for monitoring IBD (e.g., Crohn's disease and ulcerative
colitis) in a subject having the IBD disorder, wherein the method
comprises assaying the subject for the correct or therapeutic dose
of anti-TNF.alpha. antibody, i.e., the therapeutic concentration
level, over time. In this manner, it is possible to predict whether
the individual will be responsive to the therapy over the given
time period.
[0173] In certain embodiments, step (a) comprises determining the
presence and/or level of at least two, three, four, five, six,
seven, eight, nine, ten, fifteen, twenty, thirty, forty, fifty, or
more markers in the sample.
[0174] In other embodiments, the algorithm comprises a learning
statistical classifier system. In some instances, the learning
statistical classifier system is selected from the group consisting
of a random forest, classification and regression tree, boosted
tree, neural network, support vector machine, general chi-squared
automatic interaction detector model, interactive tree,
multiadaptive regression spline, machine learning classifier, and
combinations thereof. In certain instances, the statistical
algorithm comprises a single learning statistical classifier
system. In certain other instances, the statistical algorithm
comprises a combination of at least two learning statistical
classifier systems. In some instances, the at least two learning
statistical classifier systems are applied in tandem. Non-limiting
examples of statistical algorithms and analysis suitable for use in
the invention are described in International Application No.
PCT/US2011/056777, filed Oct. 18, 2011, the disclosure of which is
hereby incorporated by reference in its entirety for all
purposes.
[0175] In other embodiments, step (b) further comprises applying a
statistical algorithm to the presence and/or level of one or more
mucosal healing markers determined at an earlier time during the
course of therapy to generate an earlier mucosal healing index. In
some instances, the earlier mucosal healing index is compared to
the mucosal healing index generated in step (b) to determine a
subsequent dose of the course of therapy or whether a different
course of therapy should be administered. In certain embodiments,
the subsequent dose of the course of therapy is increased,
decreased, or maintained based upon mucosal healing index generated
in step (b). In some instances, the different course of therapy
comprises a different anti-TNF.alpha. antibody. In other instances,
the different course of therapy comprises the current course of
therapy along with an immunosuppressive agent.
[0176] In some embodiments, step (b) further comprises applying a
statistical algorithm to the presence and/or level of one or more
of the mucosal healing markers determined at an earlier time to
generate an earlier disease activity/severity index. In certain
instances, the mucosal healing index is compared to the mucosal
healing index generated in step (b) to predict the course of the
TNF-mediated disease or disorder.
[0177] In some embodiments, the method further comprises sending
the results from the selection or determination of step (d) to a
clinician. In other embodiments, step (d) comprises selecting an
initial course of therapy for the subject.
[0178] Once the diagnosis or prognosis of a subject receiving
anti-TNF drug therapy has been determined or the likelihood of
response to an anti-TNF drug has been predicted in a subject
diagnosed with a disease and disorder in which TNF has been
implicated in the pathophysiology, e.g., but not limited to, shock,
sepsis, infections, autoimmune diseases, RA, Crohn's disease,
transplant rejection and graft-versus-host disease, according to
the methods described herein, the present invention may further
comprise recommending a course of therapy based upon the diagnosis,
prognosis, or prediction. In certain instances, the present
invention may further comprise administering to a subject a
therapeutically effective amount of an anti-TNF.alpha. drug useful
for treating one or more symptoms associated with the TNF-mediated
disease or disorder. For therapeutic applications, the anti-TNF
drug can be administered alone or co-administered in combination
with one or more additional anti-TNF drugs and/or one or more drugs
that reduce the side-effects associated with the anti-TNF drug
(e.g., an immunosuppressive agent). As such, the present invention
advantageously enables a clinician to practice "personalized
medicine" by guiding treatment decisions and informing therapy
selection and optimization for anti-TNF.alpha. drugs such that the
right drug is given to the right patient at the right time.
[0179] The present invention is advantageous because it addresses
and overcomes current limitations associated with the
administration of anti-TNF drugs such as infliximab, in part, by
providing information useful for guiding treatment decisions for
those patients receiving or about to receive anti-TNF drug therapy.
In particular, the methods of the present invention find utility
for selecting an appropriate anti-TNF therapy for initial
treatment, for determining when or how to adjust or modify (e.g.,
increase or decrease) the subsequent dose of an anti-TNF drug to
optimize therapeutic efficacy and/or to reduce toxicity, for
determining when or how to combine an anti-TNF drug (e.g., at an
initial, increased, decreased, or same dose) with one or more
immunosuppressive agents such as methotrexate (MTX) or azathioprine
(AZA), and/or for determining when or how to change the current
course of therapy (e.g., switch to a different anti-TNF drug or to
a drug that targets a different mechanism).
[0180] Accordingly, the present invention is particularly useful in
the following methods of improving patient management by guiding
treatment decisions: [0181] 1. Crohn's disease prognostics: Treat
patients most likely to benefit from therapy [0182] 2.
Anti-therapeutic antibody monitoring (ATM)+Biomarker-based disease
activity profiling [0183] 3. ATM sub-stratification [0184] 4. ATM
with pharmacokinetic modeling [0185] 5. Monitor response and
predict risk of relapse: [0186] a. Avoid chronic maintenance
therapy in patients with low risk of recurrence [0187] b. Markers
of mucosal healing [0188] c. Therapy selection: Whether to combine
or not to combine anti-TNF drug therapy with an immunosuppressive
agent such as MTX or AZA [0189] 6. Patient selection for
biologics.
[0190] In some embodiments, the present invention provides a method
for measuring an inflammatory index for Crohn's Disease management
for an individual to optimize therapy, and predict response to the
anti-TNF therapeutic, the method comprising: [0191] (a)
chromatographically measuring anti-TNF therapeutics and
autoantibodies in a sample from the individual to determine their
concentration levels; [0192] (b) chromatographically measuring
anti-TNF therapeutics and autoantibodies in a sample from the
individual to determine their concentration levels; [0193] (c)
comparing the measured values to an efficacy scale to optimize
therapy, and predict response to the anti-TNF therapeutic.
[0194] In some embodiments, the present invention provides a method
for predicting the likelihood the concentration of an anti-TNF
therapeutic during the course of treatment will fall below a
threshold value, the method comprising: [0195] (a) measuring a
panel of markers selected from the group consisting of 1) GM-CSF;
2) IL-2; 3) TNF-.alpha.; 4) sTNFRII; and 5) the disease being
situated in the small intestine; and [0196] (b) predicting the
likelihood the concentration of an anti-TNF.alpha. therapeutic will
fall below the threshold based upon the concentration of the
markers.
[0197] For the purpose of illustration only, Example 5 shows an
exemplary embodiment of the present invention In particular, a
method of predicting the likelihood the concentration of an
anti-TNF treatment will fall below a threshold value.
[0198] In some embodiments, the present invention provides a method
for predicting the likelihood the concentration of an anti-TNF
therapeutic during the course of treatment will fall below a
threshold value, the method comprising: [0199] (a) measuring a
panel of markers selected from the group consisting of 1) GM-CSF;
2) IL-2; 3) TNF-.alpha.; 4) sTNFRII; and 5) the disease being
situated in the small intestine; and [0200] (b) predicting the
likelihood the concentration of an anti-TNF therapeutic will fall
below the threshold based upon the concentration of the
markers.
[0201] In other embodiments, the present invention provides a
method for predicting the likelihood that anti-drug antibodies will
occur in an individual on anti-TNF therapy, the method comprising:
[0202] (a) measuring a panel of markers selected from the group
consisting oft EGF, VEGF, IL-8, CRP and VCAM-1; and [0203] (b)
predicting the likelihood that anti-drug antibodies will occur in
an individual on anti-TNF therapy based on the concentration of
marker levels.
[0204] For the purpose of illustration only, Example 4 is an
exemplary embodiment of the present invention and demonstrates the
detectin of anti-drug antibodies to infliximab (ATI).
[0205] In other embodiments, the present invention provides a
method for monitoring an infliximab treatment regimen, the method
comprising: [0206] (a) measuring infliximab and antidrug antibodies
to infliximab (ATI); [0207] (b) measuring inflammatory markers CRP,
SAA, ICAM, VCAM; [0208] (c) measuring tissue repair marker VEGF;
and [0209] (d) correlating the measurements to therapeutic
efficacy.
[0210] For the purpose of illustration only, Example 5 is an
exemplary embodiment of the present invention and shows a method of
monitoring an IFX treatment regimen.
[0211] In other embodiments, the present invention provides a
method for determining whether an individual is a candidate for
combination therapy wherein said individual is administered
infliximab, the method comprising: [0212] (a) measuring for the
presence or absence of ATI in said individual; and [0213] (b)
administering an immunosuppressant (e.g., MTX) is the individual
has significant levels of ATI.
[0214] In yet other embodiments, the method also includes measuring
the concentration level of CRP which is indicative of the presence
of ATI. For the purpose of illustration only, Examples 6 and 7 show
that the presence and absence of ATI are predictive of responders
and non-responders of Remicade therapy. Examples 6 and 7 are
exemplary embodiments.
[0215] In yet other embodiments, the present invention provides a
method for monitoring Crohn's disease activity, the method
comprising: [0216] (a) determining an inflammatory index comprising
the measurement of a panel of markers comprising VEGF in pg/ml, CRP
in ng/ml, SAA in ng/ml, ICAM in ng/ml and VCAM in ng/ml; and [0217]
(b) comparing the index to an efficacy scale to monitor and mange
disease.
[0218] For the purpose of illustration only, Example 9 is an
exemplary embodiment and shows use of the inflammatory index.
[0219] In particular embodiments, the present invention provides
methods for determining the threshold of an anti-TNF drug such as
IFX that can best discriminate disease activity as measured by
C-reactive protein (CRP) levels. For the purpose of illustration
only, Example 12 shows that IFX dichotomized at a threshold of 3
.mu.g/ml can be differentiated by CRP. In certain instances, random
IFX<3 and IFX.gtoreq.3 .mu.g/ml serum samples have higher CRP in
IFX<3 .mu.g/ml at a 74% rate (ROC AUC). Example 12 also shows
that in ATI+ samples pairs, no significant difference in CRP
between IFX groups (above and below 3 .mu.g/ml) was observed. In
particular, CRP levels were generally higher in ATI+ sample pairs,
and CRP levels were higher in IFX<3 .mu.g/ml for ATI- samples.
Regression confirmed that CRP was positively related to ATI and
negatively related to IFX. As such, the interaction corresponds to
a CRP-IFX relationship that differs between ATI+ and ATI-.
IV. MUCOSAL HEALING INDEX
[0220] The methods of the present invention comprise monitoring
therapy response and predicting risk of relapse. In some
embodiments, the methods include detecting, measuring and/or
determining the presence and/or levels of markers of mucosal
healing.
[0221] The gut mucosa plays a key role in barrier defense in
addition to nutrient digestion, absorption and metabolism. The
dynamic processes of intestinal epithelial cell proliferation,
migration, and apoptosis are highly affected by general nutritional
status, route of feeding, and adequacy of specific nutrients in the
diet. However, with inflammatory diseases of the gut, mucosal cell
impairment can result in mucosal injury or damage, thereby
resulting in enhanced permeability to macromolecules, increased
bacterial translocation from the lumen, and stimulation of
epithelial cell apoptosis.
[0222] Mucosal injury is a multi-faceted physiological process
spanning macroscopic to molecular levels. Mucosal injury includes
the formation of macroscopically visible mucosal lesions detectable
during endoscopy, granuloma formation and disruption of the
muscularis layer at the microscopic tissue level, epithelial
apoptosis and infiltration of activated inflammatory and
lymphocytic cells at the cellular level, increased epithelial
permeability at a sub-cellular level, and gap junction disruption
at a molecular level.
[0223] Mucosal injury is likely initiated by a combination of
endogenous and environmental factors. At first stage, it is
believed that food-derived compounds, viral and bacterial-derived
factors, as well as host-derived factors, may cause epithelial cell
destruction and activation of innate and adaptive immunity. Damaged
mucosa is initially infiltrated by diverse inflammatory cells
consisting of neutrophils, eosinophils, mast cells, inflammatory
monocytes, activated macrophages and dendritic cells. Specific
adaptive immune responses toward the intestinal flora are generated
leading to the later recruitment of activated B cells, CD4+ and
CD8+ T cells to the inflamed mucosa. Neutrophils secrete elastase
which can result in extracellular matrix degradation of the
epithelium. Likewise, T cells, macrophages and intestinal
fibroblasts express inflammatory factors such as IL-1, IL-2, IL-6,
IL-14, IL-17, TGF.beta. and TNF.alpha. that lead to extracellular
matrix degradation, epithelial damage, endothelial activation,
and/or fibrosis stricture formation. Non-limiting examples of
markers of mucosal injury include matrix metalloproteases (MMPs)
and markers of oxidative stress (e.g., iNOS, reactive oxygen
metabolites).
[0224] A. Array of Mucosal Healing Markers
[0225] A variety of mucosal markers including growth factors are
particularly useful in the methods of the present invention for
personalized therapeutic management by selecting therapy,
optimizing therapy, reducing toxicity, and/or monitoring the
efficacy of therapeutic treatment with one or more therapeutic
agents such as biologics (e.g., anti-TNF drugs). In particular
embodiments, the methods described herein utilize the determination
of a mucosal healing index based upon one or more (a plurality of)
mucosal healing markers such as growth factors (e.g., alone or in
combination with biomarkers from other categories) to aid or assist
in predicting disease course, selecting an appropriate anti-TNF
drug therapy, optimizing anti-TNF drug therapy, reducing toxicity
associated with anti-TNF drug therapy, and/or monitoring the
efficacy of therapeutic treatment with an anti-TNF drug.
[0226] As such, in certain embodiments, the determination of the
presence and/or level of one or more growth factors in a sample is
useful in the present invention. As used herein, the term "growth
factor" includes any of a variety of peptides, polypeptides, or
proteins that are capable of stimulating cellular proliferation
and/or cellular differentiation.
[0227] In some embodiments, mucosal healing markers include, but
are not limited to, growth factors, inflammatory markers, cellular
adhesion markers, cytokines, anti-inflammatory markers, matrix
metalloproteinases, oxidative stress markers, and/or stress
response markers.
[0228] In some embodiments, mucosal healing markers include growth
factors. Non-limiting examples of growth factors include
amphiregulin (AREG), epiregulin (EREG), heparin binding epidermal
growth factor (HB-EGF), hepatocye growth factor (HGF),
heregulin-.beta.1 (HRG) and isoforms, neuregulins (NRG1, NRG2,
NRG3, NRG4), betacellulin (BTC), epidermal growth factor (EGF),
insulin growth factor-1 (IGF-1), transforming growth factor (TGF),
platelet-derived growth factor (PDGF), vascular endothelial growth
factors (VEGF-A, VEGF-B, VEGF-C, VEGF-D), stem cell factor (SCF),
platelet derived growth factor (PDGF), soluble fms-like tyrosine
kinase 1 (sFlt1), placenta growth factor (PIGF, PLGF or PGF),
fibroblast growth factors (FGF1, FGF2, FGF7, FGF9), and
combinations thereof. In other embodiments, mucosal healing markers
also include pigment epithelium-derived factor (PEDF, also known as
SERPINF1), endothelin-1 (ET-1), keratinocyte growth factor (KGF;
also known as FGF7), bone morphogenetic proteins (e.g.,
BMP1-BMP15), platelet-derived growth factor (PDGF), nerve growth
factor (NGF), (3-nerve growth factor (.beta.-NGF), neurotrophic
factors (e.g., brain-derived neurotrophic factor (BDNF),
neurotrophin 3 (NT3), neurotrophin 4 (NT4), etc.), growth
differentiation factor-9 (GDF-9), granulocyte-colony stimulating
factor (G-CSF), granulocyte-macrophage colony stimulating factor
(GM-CSF), myostatin (GDF-8), erythropoietin (EPO), thrombopoietin
(TPO), and combinations thereof.
[0229] In other embodiments, mucosal healing markers also include
cytokines. Non-limiting examples of cytokines that can be used to
establish a mucosal healing index include bFGF, TNF-.alpha., IL-10,
IL-12(p70), IL-1(3, IL-2, IL-6, GM-CSF, IL-13, IFN-.gamma.,
TGF-.beta.1, TGF-.beta.2, TGF-.beta.3, and combinations thereof.
Non-limiting examples of cellular adhesion markers include SAA,
CRP, ICAM, VCAM, and combinations thereof. Non-limiting examples of
anti-inflammatory markers include IL-12p70, IL-10, and combinations
thereof.
[0230] In some embodiments, mucosal healing markers include markers
specific to the gastrointestinal tract including inflammatory
markers and serology markers as described herein. Non-limiting
examples include antibodies to bacterial antigens such as, e.g.,
OmpC, flagellins (cBir-1, Fla-A, Fla-X, etc.), 12, and others
(pANCA, ASCA, etc.); anti-neutrophil antibodies, anti-Saccharomyces
cerevisiae antibodies, and anti-microbiol antibodies.
[0231] The determination of markers of oxidative stress in a sample
is also useful in the present invention. Non-limiting examples of
markers of oxidative stress include those that are protein-based or
DNA-based, which can be detected by measuring protein oxidation and
DNA fragmentation, respectively. Other examples of markers of
oxidative stress include organic compounds such as
malondialdehyde.
[0232] Oxidative stress represents an imbalance between the
production and manifestation of reactive oxygen species and a
biological system's ability to readily detoxify the reactive
intermediates or to repair the resulting damage. Disturbances in
the normal redox state of tissues can cause toxic effects through
the production of peroxides and free radicals that damage all
components of the cell, including proteins, lipids, and DNA. Some
reactive oxidative species can even act as messengers through a
phenomenon called redox signaling.
[0233] In certain embodiments, derivatives of reactive oxidative
metabolites (DROMs), ratios of oxidized to reduced glutathione (Eh
GSH), and/or ratios of oxidized to reduced cysteine (Eh CySH) can
be used to quantify oxidative stress. See, e.g., Neuman et al.,
Clin. Chem., 53:1652-1657 (2007). Oxidative modifications of highly
reactive cysteine residues in proteins such as tyrosine
phosphatases and thioredoxin-related proteins can also be detected
or measured using a technique such as, e.g., mass spectrometry
(MS). See, e.g., Naito et al., Anti-Aging Medicine, 7 (5):36-44
(2010). Other markers of oxidative stress include protein-bound
acrolein as described, e.g., in Uchida et al., PNAS, 95 (9)
4882-4887 (1998), the free oxygen radical test (FORT), which
reflects levels of organic hydroperoxides, and the redox potential
of the reduced glutathione/glutathione disulfide couple, (Eh)
GSH/GSSG. See, e.g., Abramson et al., Atherosclerosis,
178(1):115-21 (2005).
[0234] In some embodiments, matrix metalloproteinases (MMPs)
include members of a family of Zn.sup.2+-dependent extracellular
matrix (ECM) degrading endopeptidases that are able to degrade all
types of ECM proteins. Non-limiting examples of MMPs include MMP-1,
MMP-2, MMP-3, MMP-7, MMP-8, MMP-9, MMP-12, MMP-13, MT1-MMP-1, and
combinations thereof. It has been shown that MMP-3 and MMP-9 are
associated with mucosal injury and fistulae in CD patients (Baugh
et al., Gastroenterology, 117: 814-822, (1999); Bailey et al., J.
Clin. Pathol., 47: 113-116 (1994)). In some embodiments, stress
response markers include markers of oxidative stress, such as
reactive oxygen species (ROS), superoxide dismutase (SOD), catalase
(CAT), and glutathione, and markers of endoplasmic reticulum (ER)
stress. Non-limiting examples of markers of oxidative stress
include those that are protein-based or DNA-based, which can be
detected by measuring protein oxidation and DNA fragmentation,
respectively. In other embodiments, mucosal healing markers further
include markers of oxidative DNA and/or protein damage.
Non-limiting examples of ER stress markers include markers of
unfolded protein response (e.g., ATF6, HSPA5, PDIA4, XBP1, IRE1,
PERK, EIF2A, GADD34, GRP-78, phosphoylated JNK, caspase-12,
caspase-3, and combinations thereof).
[0235] The human amphiregulin (AREG) polypeptide sequence is set
forth in, e.g., Genbank Accession Nos. NP_001648.1 and
XP_001125684.1. The human AREG mRNA (coding) sequence is set forth
in, e.g., Genbank Accession Nos. NM_001657.2 and XM_001125684.3.
One skilled in the art will appreciate that AREG is also known as
AR, colorectum cell-derived growth factor, CRDGF, SDGF, and
AREGB.
[0236] The human epiregulin (EREG) polypeptide sequence is set
forth in, e.g., Genbank Accession No. NP.sub.13 001423.1. The human
EREG mRNA (coding) sequence is set forth in, e.g., Genbank
Accession No. NM_001432.2. One skilled in the art will appreciate
that EREG is also known as EPR.
[0237] The human heparin-binding EGF-like growth factor (HB-EGF)
polypeptide sequence is set forth in, e.g., Genbank Accession No.
NP_001936.1. The human HB-EGF mRNA (coding) sequence is set forth
in, e.g., Genbank Accession No. NM_001945.2. One skilled in the art
will appreciate that HB-EGF is also known as diphtheria toxin
receptor, DT-R, HBEGF, DTR, DTS, and HEGFL.
[0238] The human hepatocyte growth factor (HGF) polypeptide
sequence is set forth in, e.g., Genbank Accession Nos. NP_000592.3,
NP_001010931.1, NP_001010932.1, NP_001010933.1, and NP_001010934.1.
The human HGF mRNA (coding) sequence is set forth in, e.g., Genbank
Accession Nos. NM_000601.4, NM_001010931.1, NM_001010932.1,
NM_001010933.1 and NM_001010934.1. One skilled in the art will
appreciate that HGF is also known as scatter factor, SF, HPTA and
hepatopoietin-A. One of skill will also appreciate that HGF
includes to all isoform variants.
[0239] The human neuregulin-1 (NRG1) polypeptide sequence is set
forth in, e.g., Genbank Accession Nos., NP_001153467.1,
NP_001153471.1, NP_001153473.1, NP_001153477.1, NP_039250.2,
NP_039251.2, NP_039252.2, NP_039253.1, NP_039254.1, NP_039256.2,
and NP_039258.1. The human NRG1 mRNA (coding) sequence is set forth
in, e.g., Genbank Accession No. NM_001159995.1, NM_001159999.1,
NM_001160001.1, NM_001160005.1, NM_013956.3, NM_013957.3,
NM_013958.3, NM_013959.3, NM_013960.3, NM_013962.2, and
NM_013964.3. One skilled in the art will appreciate that NRG1 is
also known as GGF, HGL, HRGA, NDF, SMDF, ARIA, acetylcholine
receptor-inducing activity, breast cancer cell differentiation
factor p45, glial growth factor, heregulin, HRG, neu
differentiation factor, and sensory and motor neuron-derived
factor. One of skill will also appreciate that NRG1 includes to all
isoform variants.
[0240] The human neuregulin-2 (NRG2) polypeptide sequence is set
forth in, e.g., Genbank Accession Nos. NP_001171864.1, NP_004874.1,
NP_053584.1, NP_053585.1 and NP_053586.1. The human NRG2 mRNA
(coding) sequence is set forth in, e.g., Genbank Accession Nos.
NM_001184935.1, NM_004883.2, NM_013981.3, NM_013982.2 and
NM_013983.2. One skilled in the art will appreciate that NRG2 is
also known as NTAK, neural- and thymus-derived activator for ERBB
kinases, DON-1, and divergent of neuregulin-1. One of skill will
also appreciate that NRG2 includes to all isoform variants.
[0241] The human neuregulin-3 (NRG3) polypeptide sequence is set
forth in, e.g., Genbank Accession Nos. NP.sub.13 001010848.2 and
NP_001159445.1. The human NRG3 mRNA (coding) sequence is set forth
in, e.g., Genbank Accession Nos. NM_001010848.3 and NM_001165973.1.
One skilled in the art will appreciate that NRG2 includes to all
isoform variants.
[0242] The human neuregulin-4 (NRG4) polypeptide sequence is set
forth in, e.g., Genbank Accession No. NP_612640.1. The human NRG4
mRNA (coding) sequence is set forth in, e.g., Genbank Accession No.
NM_138573.3. One skilled in the art will appreciate that NRG4
includes to all isoform variants.
[0243] The human betacellulin (BTC) polypeptide sequence is set
forth in, e.g., Genbank Accession No. NP_001720.1. The human BTC
mRNA (coding) sequence is set forth in, e.g., Genbank Accession No.
NM_001729.2. One skilled in the art will appreciate that BTC
includes to all isoform variants.
[0244] The human epidermal growth factor (EGF) polypeptide sequence
is set forth in, e.g., Genbank Accession Nos. NP_001954.2 and
NP_001171602.1. The human EGF mRNA (coding) sequence is set forth
in, e.g., Genbank Accession Nos. NM_001963.4 and NM_001178131.1.
One skilled in the art will appreciate that EGF is also known as
beta-urogastrone, urogastrone, URG, and HOMG4.
[0245] The human insulin-like growth factor (IGF) polypeptide
sequence is set forth in, e.g., Genbank Accession Nos. NP_000609.1
and NP_001104755.1. The human IGF mRNA (coding) sequence is set
forth in, e.g., Genbank Accession No. NM_000618.3 and
NM_001111285.1. One skilled in the art will appreciate that IGF
includes to all isoform variants. One skilled in the art will also
appreciate that IGF is also known as mechano growth factor, MGF and
somatomedin-C.
[0246] The human transforming growth factor alpha (TGF-.alpha.)
polypeptide sequence is set forth in, e.g., Genbank Accession Nos.
NP_003227.1 and NP_001093161.1. The human TGF-.alpha. mRNA (coding)
sequence is set forth in, e.g., Genbank Accession Nos. NM_003236.3
and NM_001099691.2. One skilled in the art will appreciate that
TGF-.alpha. includes to all isoform variants. One skilled in the
art will also appreciate that TGF-.alpha. is also known as EGF-like
TGF, ETGF, and TGF type 1.
[0247] The human vascular endothelial growth factor (VEGF-A)
polypeptide sequence is set forth in, e.g., Genbank Accession Nos.
NP_001020537, NP_001020538, NP_001020539, NP_001020540,
NP_001020541, NP_001028928, and NP_003367. The human VEGF-A mRNA
(coding) sequence is set forth in, e.g., Genbank Accession No.
NM_001025366, NM_001025367, NM_001025368, NM_001025369,
NM_001025370, NM_001033756, and NM_003376. One skilled in the art
will appreciate that VEGF-A is also known as VPF, VEGFA, VEGF, and
MGC70609. One skilled in the art will appreciate that VEGF-A
includes to all isoform variants.
[0248] The human vascular endothelial growth factor (VEGF-B)
polypeptide sequence is set forth in, e.g., Genbank Accession Nos.
NP_001230662, and NP_003368. The human VEGF-B mRNA (coding)
sequence is set forth in, e.g., Genbank Accession Nos. NM_001243733
and NM_003377. One skilled in the art will appreciate that VEGF-B
is also known as VEGFB, VEGF-related factor, and VRF. One skilled
in the art will appreciate that VEGF-B includes to all isoform
variants.
[0249] The human vascular endothelial growth factor (VEGF-C)
polypeptide sequence is set forth in, e.g., Genbank Accession No.
NP_005420. The human VEGF-C mRNA (coding) sequence is set forth in,
e.g., Genbank Accession No. NM_005429. One skilled in the art will
appreciate that VEGF-C is also known as Flt4 ligand, Flt4-L, VRP
and vascular endothelial growth factor-realted protein. One skilled
in the art will appreciate that VEGF-C includes to all isoform
variants.
[0250] The human fibroblast growth factor 1 (FGF1) polypeptide
sequence is set forth in, e.g., Genbank Accession Nos. NP_000791,
NP_001138364, NP_001138406, NP_001138407, NP_001138407, NP_149127,
and NP_149128. The human FGF1 mRNA (coding) sequence is set forth
in, e.g., Genbank Accession Nos. NM_000800, NM_001144892,
NM_001144934, NM_001144934, NM_001144935, NM_033136 and NM_033137.
One skilled in the art will appreciate that FGF1 is also known as
FGFA, FGF-1, acidic fibroblast growth factor, aFGF, endothelial
cell growth factor, ECGF, heparin-binding growth factor 1, and
HB-EGF1. One skilled in the art will appreciate that FGF1 includes
to all isoform variants.
[0251] The human basic fibroblast growth factor (bFGF) polypeptide
sequence is set forth in, e.g., Genbank Accession No. NP_001997.5.
The human bFGF mRNA (coding) sequence is set forth in, e.g.,
Genbank Accession No. NM_002006.4. One skilled in the art will
appreciate that bFGF is also known as FGF2, FGFB, and HBGF-2.
[0252] The human fibroblast growth factor 7 (FGF7) polypeptide
sequence is set forth in, e.g., Genbank Accession No. NP_002000.1.
The human FGF7 mRNA (coding) sequence is set forth in, e.g.,
Genbank Accession No. NM_002009.3. One skilled in the art will
appreciate that FGF7 is also known as FGF-7, HBGF-7 and
keratinocyte growth factor.
[0253] The human fibroblast growth factor 9 (FGF9) polypeptide
sequence is set forth in, e.g., Genbank Accession No. NP_002001.1.
The human FGF9 mRNA (coding) sequence is set forth in, e.g.,
Genbank Accession No. NM_002010.2. One skilled in the art will
appreciate that FGF9 is also known as FGF-9, GAF, and HBGF-9.
[0254] The human TNF-related weak inducer of apoptosis (TWEAK)
polypeptide sequence is set forth in, e.g., Genbank Accession No.
NP_003800.1. The human TWEAK mRNA (coding) sequence is set forth
in, e.g., Genbank Accession No. NM_003809.2. One skilled in the art
will appreciate that TWEAK is also known as TNF12, APO3 ligand,
APO3L, DR3LG, and UNQ181/PRO207.
[0255] In certain instances, the presence or level of a particular
mucosal healing marker such as a growth factor is detected at the
level of mRNA expression with an assay such as, for example, a
hybridization assay or an amplification-based assay. In certain
other instances, the presence or level of a particular growth
factor is detected at the level of protein expression using, for
example, an immunoassay (e.g., ELISA) or an immunohistochemical
assay. In an exemplary embodiment, the presence or level of a
particular growth factor is detected using a multiplexed
immunoarray, such as a Collaborative Enzyme Enhanced Reactive
ImmunoAssay (CEER), also known as the Collaborative Proximity
Immunoassay (COPIA). CEER is described in the following patent
documents which are herein incorporated by reference in their
entirety for all purposes: PCT Publication No. WO 2008/036802; PCT
Publication No. WO 2009/012140; PCT Publication No. WO 2009/108637;
PCT Publication No. WO 2010/132723; PCT Publication No. WO
2011/008990; and PCT Application No. PCT/US2010/053386, filed Oct.
20, 2010. Suitable ELISA kits for determining the presence or level
of a growth factor in a serum, plasma, saliva, or urine sample are
available from, e.g., Antigenix America Inc. (Huntington Station,
N.Y.), Promega (Madison, Wis.), R&D Systems, Inc. (Minneapolis,
Minn.), Invitrogen (Camarillo, Calif.), CHEMICON International,
Inc. (Temecula, Calif.), Neogen Corp. (Lexington, Ky.), PeproTech
(Rocky Hill, N.J.), Alpco Diagnostics (Salem, N.H.), Pierce
Biotechnology, Inc. (Rockford, Ill.), and/or Abazyme (Needham,
Mass.).
[0256] In particular embodiments, at least one or a plurality
(e.g., 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 ,15 , 16, 17, 18,
19, 20, or 21, such as, e.g., a panel or an array) of the following
growth factor markers can be detected (e.g., alone or in
combination with biomarkers from other categories) to aid or assist
in predicting disease course, and/or to improve the accuracy of
selecting therapy, optimizing therapy, reducing toxicity, and/or
monitoring the efficacy of therapeutic treatment to anti-TNF drug
therapy: AREG, EREG, HB-EGF, HGF, NRG1, NRG2, NRG3, NRG4, BTC, EGF,
IGF, TGF-.alpha., VEGF-A, VEGF-B, VEGF-C, VEGF-D, FGF1, FGF2, FGF7,
FGF9, TWEAK and combinations thereof.
[0257] B. Mucosal Healing Index
[0258] In certain aspects, the present invention provides an
algorithmic-based analysis of one or a plurality of (e.g., 2, 3, 4,
5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, or
more) mucosal healing markers to improve the accuracy of selecting
therapy, optimizing therapy, reducing toxicity, and/or monitoring
the efficacy of therapeutic treatment to anti-TNF.alpha. drug
therapy.
[0259] A single statistical algorithm or a combination of two or
more statistical algorithms described herein can then be applied to
the presence or concentration level of the mucosal healing markers
detected, measured, or determined in the sample to thereby select
therapy, optimize therapy, reduce toxicity, or monitor the efficacy
of therapeutic treatment with an anti-TNF.alpha. drug. As such, the
methods of the invention find utility in determining patient
management by determining patient immune status.
[0260] In some embodiments, the statistical algorithm comprises a
learning statistical classifier system. In some instances, the
learning statistical classifier system is selected from the group
consisting of a random forest, classification and regression tree,
boosted tree, neural network, support vector machine, general
chi-squared automatic interaction detector model, interactive tree,
multiadaptive regression spline, machine learning classifier, and
combinations thereof. In certain instances, the statistical
algorithm comprises a single learning statistical classifier
system. In other embodiments, the statistical algorithm comprises a
combination of at least two learning statistical classifier
systems. In some instances, the at least two learning statistical
classifier systems are applied in tandem. Non-limiting examples of
statistical algorithms and analysis suitable for use in the
invention are described in International Application No.
PCT/US2011/056777, filed Oct. 18, 2011, the disclosure of which is
hereby incorporated by reference in its entirety for all
purposes.
[0261] Preferably, mucosal healing index is an empirically derived
experimentally prepared index of values. In some instances, the
index of values is transformed from an array of control
measurements that were experimentally determined. In one aspect,
the concentration of markers or their measured concentration values
are transformed into an index by an algorithm resident on a
computer. In certain aspects, the index is a synthetic or human
derived output, score, or cut off value(s), which expresses the
biological data in numerical terms. The index can be used to
determine or make or aid in making a clinical decision. A mucosal
healing index can be measured multiple times over the course of
time. In one aspect, the algorithm can be trained with known
samples and thereafter validated with samples of known
identity.
[0262] In further embodiments, the method for assessing or
measuring mucosal healing further comprises comparing the
determined level of the mucosal healing marker present in a sample
to an index value or cutoff value or reference value or threshold
value, wherein the level of the mucosal healing marker above or
below that value is predictive or indicative of an increased or
higher likelihood of the subject either undergoing mucosal healing
or not undergoing mucosal healing. One skilled in the art will
understand that the index value or cutoff value or reference value
or threshold value is in units such as mg/ml, .mu.g/ml, ng/ml,
pg/ml, fg/ml, EU/ml, or U/ml depending on the marker of interest
that is being measured.
[0263] In some embodiments, the mucosal healing index control is a
mucosal healing index derived from a healthy individual, or an
individual who has progressed from a disease state to a healthy
state. Alternatively, the control can be an index representing a
time course of a more diseased state or healthy to disease.
[0264] In some embodiments, the methods of determining the course
of therapy and the like include the use of an empirically derived
index, score or analysis to select for example, selecting a dose of
drug, selecting an appropriate drug, or a course or length of
therapy, a therapy regimen, or maintenance of an existing drug or
dose. In certain aspects, a derived or measured index can be used
to determine the course of therapy.
[0265] Understanding the clinical course of disease will enable
physicians to make better informed treatment decisions for their
inflammatory disease patients (e.g., IBD, Crohn's disease or
ulcerative colitis) and may help to direct new drug development in
the future. The ideal mucosal healing marker(s) for use in the
mucosal healing index described herein should be able to identify
individuals at risk for the disease and should be disease-specific.
Moreover, mucosal healing marker(s) should be able to detect
disease activity and monitor the effect of treatment; and should
have a predictive value towards relapse or recurrence of the
disease. Predicting disease course, however, has now been expanded
beyond just disease recurrence, but perhaps more importantly to
include predictors of disease complications including surgery. The
present invention is particularly advantageous because it provides
indicators of mucosal healing and enables a prediction of the risk
of relapse in those patients in remission. In addition, the mucosal
healing markers and mucosal healing index of present invention have
enormous implications for patient management as well as therapeutic
decision-making and would aid or assist in directing the
appropriate therapy to those patients who would most likely benefit
from it and avoid the expense and potential toxicity of chronic
maintenance therapy in those who have a low risk of recurrence.
I. DISEASE ACTIVITY PROFILE
[0266] As described herein, the disease activity profile (DAP) of
the present invention can advantageously be used in methods for
personalized therapeutic management of a disease in order to
optimize therapy and/or monitor therapeutic efficacy. In certain
embodiments, the methods of the invention can improve the accuracy
of selecting therapy, optimizing therapy, reducing toxicity, and/or
monitoring the efficacy of therapeutic treatment to anti-TNF drug
therapy. In particular embodiments, the DAP is determined by
measuring an array of one or a plurality of (e.g., 2, 3, 4, 5, 6,
7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23,
24, 25, 30, 35, 40, 45, 50, or more) markers at a plurality of time
points over the course of therapy with a therapeutic antibody
(e.g., anti-TNF drug) to determine a DAP, wherein the DAP comprises
a representation of the concentration level of each marker over
time. In certain embodiments, the DAP may comprise a representation
of the presence or absence, concentration (e.g., expression) level,
activation (e.g., phosphorylation) level, and/or velocity value
(e.g., change in slope of the level of a particular marker) of each
marker over time. As such, the methods of the present invention
find utility in determining patient management by determining
patient immune status.
[0267] In certain instances, a single statistical algorithm or a
combination of two or more statistical algorithms can be applied to
the concentration level of each marker over the course of therapy
or to the DAP itself.
[0268] Understanding the clinical course of disease enables
physicians to make better informed treatment decisions for their
inflammatory disease patients (e.g., IBD (e.g., Crohn's disease),
rheumatoid arthritis (RA), others) and helps to direct new drug
development. The ideal biomarker(s) for use in the disease activity
profile described herein is able to identify individuals at risk
for the disease and is disease-specific. Moreover, the biomarker(s)
are able to detect disease activity and monitor the effect of
treatment; and have a predictive value towards relapse or
recurrence of the disease. Predicting disease course, however, has
now been expanded beyond just disease recurrence, but more
importantly to include predictors of disease complications
including surgery. The present invention is particularly
advantageous because it provides indicators of disease activity
and/or severity and enables a prediction of the risk of relapse in
those patients in remission. In addition, the biomarkers and
disease activity profile of the present invention have enormous
implications for patient management, as well as therapeutic
decision-making, and aid or assist in directing the appropriate
therapy to patients who most likely will benefit from it and avoid
the expense and potential toxicity of chronic maintenance therapy
in those who have a low risk of recurrence.
[0269] As a non-limiting example, the disease activity profile
(DAP) in one embodiment comprises detecting, measuring, or
determining the presence, level (concentration (e.g., total) and/or
activation (e.g., phosphorylation)), or genotype of one or more
specific biomarkers in one or more of the following categories of
biomarkers: [0270] (1) Drug levels (e.g., anti-TNF drug levels);
[0271] (2) Anti-drug antibody (ADA) levels (e.g., level of
autoantibody to an anti-TNF drug); [0272] (3) Inflammatory markers;
[0273] (4) Anti-inflammatory markers; and/or [0274] (5) Tissue
repair markers.
[0275] Non-limiting examples of additional and/or alternative
markers in which the presence, level (concentration (e.g., total)
and/or activation (e.g., phosphorylation)), or genotype can be
measured include: [0276] (6) Serology (e.g., immune markers);
[0277] (7) Markers of oxidative stress; [0278] (8) Cell surface
receptors (e.g., CD64, others); [0279] (9) Signaling pathways;
[0280] (10) kel, or the elimination rate constant of a drug such as
a therapeutic antibody (e.g., infliximab); and/or [0281] (11) Other
markers (e.g., genetic markers such as inflammatory pathway
genes).
[0282] A. Anti-TNF Drug Levels & Anti-Drug Antibody (ADA)
Levels
[0283] In some embodiments, the disease activity profile (DAP)
comprises determining the presence and/or level of anti-TNF drug
(e.g., level of free anti-TNF.alpha. therapeutic antibody such as
infliximab) and/or anti-drug antibody (ADA) (e.g., level of
autoantibody to the anti-TNF drug such as HACA) in a patient sample
(e.g., a serum sample from a patient on anti-TNF drug therapy) at
multiple time points, e.g., before, during, and/or after the course
of therapy.
[0284] In particular embodiments, the presence and/or level of
anti-TNF drug and/or ADA is determined with a homogeneous mobility
shift assay using size exclusion chromatography. This method, which
is described in PCT Application No. PCT/US2010/054125, filed Oct.
26, 2010, the disclosure of which is hereby incorporated by
reference in its entirety for all purposes, is particularly
advantageous for measuring the presence or level of TNF.alpha.
inhibitors as well as autoantibodies (e.g., HACA, HAHA, etc.) that
are generated against them.
[0285] In one embodiment, the method for detecting the presence of
an anti-TNF.alpha. antibody in a sample comprises:
[0286] (a) contacting labeled TNF.alpha. with a sample having or
suspected of having an anti-TNF.alpha. antibody to form a labeled
complex with the anti-TNF.alpha. antibody;
[0287] (b) subjecting the labeled complex to size exclusion
chromatography to separate the labeled complex; and
[0288] (c) detecting the labeled complex, thereby detecting the
anti-TNF.alpha. antibody.
[0289] In certain instances, the methods are especially useful for
the following anti-TNF.alpha. antibodies: REMICADE.TM.
(infliximab), ENBREL.TM. (etanercept), HUMIRA.TM. (adalimumab), and
CIMZIA.RTM. (certolizumab pegol).
[0290] Tumor necrosis factor .alpha. (TNF.alpha.) is a cytokine
involved in systemic inflammation and is a member of a group of
cytokines that stimulate the acute phase reaction. The primary role
of TNF.alpha. is in the regulation of immune cells. TNF.alpha. is
also able to induce apoptotic cell death, to induce inflammation,
and to inhibit tumorigenesis and viral replication. TNF is
primarily produced as a 212-amino acid-long type II transmembrane
protein arranged in stable homotrimers.
[0291] The terms "TNF", "TNF.alpha.," and "TNF-.alpha.," as used
herein, are intended to include a human cytokine that exists as a
17 kDa secreted form and a 26 kDa membrane associated form, the
biologically active form of which is composed of a trimer of
noncovalently bound 17 kDa molecules. The structure of TNF-.alpha.
is described further in, for example, Jones, et al. (1989) Nature,
338:225-228. The term TNF-.alpha. is intended to include human, a
recombinant human TNF-.alpha. (rhTNF-.alpha.), or at least about
80% identity to the human TNF.alpha. protein. Human TNF.alpha.
consists of a 35 amino acid (aa) cytoplasmic domain, a 21 aa
transmembrane segment, and a 177 aa extracellular domain (ECD)
(Pennica, D. et al. (1984) Nature 312:724). Within the ECD, human
TNF.alpha. shares 97% aa sequence identity with rhesus and 71% 92%
with bovine, canine, cotton rat, equine, feline, mouse, porcine,
and rat TNF.alpha.. TNF.alpha. can be prepared by standard
recombinant expression methods or purchased commercially (R & D
Systems, Catalog No. 210-TA, Minneapolis, Minn.).
[0292] In certain instances, after the TNF .alpha. antibody is
detected, the TNF .alpha. antibody is measured using a standard
curve.
[0293] In another embodiment, the method for detecting an
autoantibody to an anti-TNF.alpha. antibody in a sample
comprises:
[0294] (a) contacting labeled anti-TNF.alpha. antibody with the
sample to form a labeled complex with the autoantibody;
[0295] (b) subjecting the labeled complex to size exclusion
chromatography to separate the labeled complex; and
[0296] (c) detecting the labeled complex, thereby detecting the
autoantibody.
[0297] In certain instances, the autoantibodies include human
anti-chimeric antibodies (HACA), human anti-humanized antibodies
(HAHA), and human anti-mouse antibodies (HAMA).
[0298] Non-limiting examples of other methods for determining the
presence and/or level of anti-TNF drug and/or anti-drug antibodies
(ADA) include enzyme-linked immunosorbent assays (ELISAs) such as
bridging ELISAs. For example, the Infliximab ELISA from Matriks
Biotek Laboratories detects free infliximab in serum and plasma
samples, and the HACA ELISA from PeaceHealth Laboratories detects
HACA in serum samples.
[0299] B. Inflammatory Markers
[0300] Although disease course of an inflammatory disease is
typically measured in terms of inflammatory activity by noninvasive
tests using white blood cell count, this method has a low
specificity and shows limited correlation with disease
activity.
[0301] As such, in certain embodiments, a variety of inflammatory
markers, including biochemical markers, serological markers,
protein markers, genetic markers, and/or other clinical or
echographic characteristics, are particularly useful in the methods
of the present invention for personalized therapeutic management by
selecting therapy, optimizing therapy, reducing toxicity, and/or
monitoring the efficacy of therapeutic treatment with one or more
therapeutic agents such as biologics (e.g., anti-TNF drugs). In
particular embodiments, the methods described herein utilize the
determination of a disease activity profile (DAP) based upon one or
more (a plurality of) inflammatory markers (e.g., alone or in
combination with biomarkers from other categories) to aid or assist
in predicting disease course, selecting an appropriate anti-TNF
drug therapy, optimizing anti-TNF drug therapy, reducing toxicity
associated with anti-TNF drug therapy, and/or monitoring the
efficacy of therapeutic treatment with an anti-TNF drug.
[0302] Non-limiting examples of inflammatory markers include
cytokines, chemokines, acute phase proteins, cellular adhesion
molecules, S100 proteins, and/or other inflammatory markers. In
preferred embodiments, the inflammatory markers comprise at least
1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, or more cytokines. In
one particular embodiment, the cytokines are at least 1, 2, 3, 4,
5, 6, 7, or all 8 of the following: GM-CSF, IFN-.gamma.,
IL-1.beta., IL-2, IL-6, IL-8, TNF-.alpha., and sTNF RII.
[0303] 1. Cytokines and Chemokines
[0304] The determination of the presence or level of at least one
cytokine or chemokine in a sample is particularly useful in the
present invention. As used herein, the term "cytokine" includes any
of a variety of polypeptides or proteins secreted by immune cells
that regulate a range of immune system functions and encompasses
small cytokines such as chemokines. The term "cytokine" also
includes adipocytokines, which comprise a group of cytokines
secreted by adipocytes that function, for example, in the
regulation of body weight, hematopoiesis, angiogenesis, wound
healing, insulin resistance, the immune response, and the
inflammatory response.
[0305] In certain embodiments, the presence or level of at least
one cytokine including, but not limited to, granulocyte-macrophage
colony-stimulating factor (GM-CSF), IFN-.gamma., IL-1.gamma., IL-2,
IL-6, IL-8, TNF-.alpha., soluble tumor necrosis factor-.alpha.
receptor II (sTNF RII), TNF-related weak inducer of apoptosis
(TWEAK), osteoprotegerin (OPG), IFN-.alpha., IFN-.beta.,
IL-1.alpha., IL-1 receptor antagonist (IL-1ra), IL-4, IL-5, soluble
IL-6 receptor (sIL-6R), IL-7, IL-9, IL-12, IL-13, IL-15, IL-17,
IL-23, and IL-27 is determined in a sample.
[0306] In certain other embodiments, the presence or level of at
least one chemokine such as, for example, CXCL1/GRO1/GRO.alpha.,
CXCL2/GRO2, CXCL3/GRO3, CXCL4/PF-4, CXCL5/ENA-78, CXCL6/GCP-2,
CXCL7/NAP-2, CXCL9/MIG, CXCL10/IP-10, CXCL11/I-TAC, CXCL12/SDF-1,
CXCL13/BCA-1, CXCL14/BRAK, CXCL15, CXCL16, CXCL17/DMC, CCL1,
CCL2/MCP-1, CCL3/MIP-1.alpha., CCL4/MIP-1.beta., CCL5/RANTES,
CCL6/C10, CCL7/MCP-3, CCL8/MCP-2, CCL9/CCL10, CCL11/Eotaxin,
CCL12/MCP-5, CCL13/MCP-4, CCL14/HCC-1, CCL15/MIP-5, CCL16/LEC,
CCL17/TARC, CCL18/MIP-4, CCL19/MIP-3.beta., CCL20/MIP-3.alpha.,
CCL21/SLC, CCL22/MDC, CCL23/MPIF1, CCL24/Eotaxin-2, CCL25/TECK,
CCL26/Eotaxin-3, CCL27/CTACK, CCL28/MEC, CL1, CL2, and CX3CL1 is
determined in a sample. In certain further embodiments, the
presence or level of at least one adipocytokine including, but not
limited to, leptin, adiponectin, resistin, active or total
plasminogen activator inhibitor-1 (PAI-1), visfatin, and retinol
binding protein 4 (RBP4) is determined in a sample. Preferably, the
presence or level of GM-CSF, IFN-.gamma., IL-1.beta., IL-2, IL-6,
IL-8, TNF-.alpha., sTNF RII, and/or other cytokines or chemokines
is determined.
[0307] In certain instances, the presence or level of a particular
cytokine or chemokine is detected at the level of mRNA expression
with an assay such as, for example, a hybridization assay or an
amplification-based assay. In certain other instances, the presence
or level of a particular cytokine or chemokine is detected at the
level of protein expression using, for example, an immunoassay
(e.g., ELISA) or an immunohistochemical assay. Suitable ELISA kits
for determining the presence or level of a cytokine or chemokine of
interest in a serum, plasma, saliva, or urine sample are available
from, e.g., R&D Systems, Inc. (Minneapolis, Minn.), Neogen
Corp. (Lexington, Ky.), Alpco Diagnostics (Salem, N.H.), Assay
Designs, Inc. (Ann Arbor, Mich.), BD Biosciences Pharmingen (San
Diego, Calif.), Invitrogen (Camarillo, Calif.), Calbiochem (San
Diego, Calif.), CHEMICON International, Inc. (Temecula, Calif.),
Antigenix America Inc. (Huntington Station, N.Y.), QIAGEN Inc.
(Valencia, Calif.), Bio-Rad Laboratories, Inc. (Hercules, Calif.),
and/or Bender MedSystems Inc. (Burlingame, Calif.).
[0308] The human IL-6 polypeptide sequence is set forth in, e.g.,
Genbank Accession No. NP_000591. The human IL-6 mRNA (coding)
sequence is set forth in, e.g., Genbank Accession No. NM_000600.
One skilled in the art will appreciate that IL-6 is also known as
interferon beta 2 (IFNB2), HGF, HSF, and BSF2.
[0309] The human IL-1.beta. polypeptide sequence is set forth in,
e.g., Genbank Accession No. NP_000567. The human IL-1.beta. mRNA
(coding) sequence is set forth in, e.g., Genbank Accession No.
NM_000576. One skilled in the art will appreciate that IL-1.beta.
is also known as IL1F2 and IL-1beta.
[0310] The human IL-8 polypeptide sequence is set forth in, e.g.,
Genbank Accession No. NP_000575 (SEQ ID NO:1). The human IL-8 mRNA
(coding) sequence is set forth in, e.g., Genbank Accession No.
NM_000584 (SEQ ID NO:2). One skilled in the art will appreciate
that IL-8 is also known as CXCL8, K60, NAF, GCP1, LECT, LUCT, NAP1,
3-10C, GCP-1, LYNAP, MDNCF, MONAP, NAP-1, SCYB8, TSG-1, AMCF-I, and
b-ENAP.
[0311] The human TWEAK polypeptide sequence is set forth in, e.g.,
Genbank Accession Nos. NP_003800 and AAC51923. The human TWEAK mRNA
(coding) sequence is set forth in, e.g., Genbank Accession Nos.
NM_003809 and BC104420. One skilled in the art will appreciate that
TWEAK is also known as tumor necrosis factor ligand superfamily
member 12 (TNFSF12), APO3 ligand (APO3L), CD255, DR3 ligand, growth
factor-inducible 14 (Fn14) ligand, and UNQ181/PRO207.
[0312] 2. Acute Phase Proteins
[0313] The determination of the presence or level of one or more
acute-phase proteins in a sample is also useful in the present
invention. Acute-phase proteins are a class of proteins whose
plasma concentrations increase (positive acute-phase proteins) or
decrease (negative acute-phase proteins) in response to
inflammation. This response is called the acute-phase reaction
(also called acute-phase response). Examples of positive
acute-phase proteins include, but are not limited to, C-reactive
protein (CRP), D-dimer protein, mannose-binding protein, alpha
1-antitrypsin, alpha 1-antichymotrypsin, alpha 2-macroglobulin,
fibrinogen, prothrombin, factor VIII, von Willebrand factor,
plasminogen, complement factors, ferritin, serum amyloid P
component, serum amyloid A (SAA), orosomucoid (alpha 1-acid
glycoprotein, AGP), ceruloplasmin, haptoglobin, and combinations
thereof. Non-limiting examples of negative acute-phase proteins
include albumin, transferrin, transthyretin, transcortin,
retinol-binding protein, and combinations thereof. Preferably, the
presence or level of CRP and/or SAA is determined.
[0314] In certain instances, the presence or level of a particular
acute-phase protein is detected at the level of mRNA expression
with an assay such as, for example, a hybridization assay or an
amplification-based assay. In certain other instances, the presence
or level of a particular acute-phase protein is detected at the
level of protein expression using, for example, an immunoassay
(e.g., ELISA) or an immunohistochemical assay. For example, a
sandwich colorimetric ELISA assay available from Alpco Diagnostics
(Salem, N.H.) can be used to determine the level of CRP in a serum,
plasma, urine, or stool sample. Similarly, an ELISA kit available
from Biomeda Corporation (Foster City, Calif.) can be used to
detect CRP levels in a sample. Other methods for determining CRP
levels in a sample are described in, e.g., U.S. Pat. Nos. 6,838,250
and 6,406,862; and U.S. Patent Publication Nos. 20060024682 and
20060019410. Additional methods for determining CRP levels include,
e.g., immunoturbidimetry assays, rapid immunodiffusion assays, and
visual agglutination assays. Suitable ELISA kits for determining
the presence or level of SAA in a sample such as serum, plasma,
saliva, urine, or stool are available from, e.g., Antigenix America
Inc. (Huntington Station, N.Y.), Abazyme (Needham, Mass.), USCN
Life (Missouri City, Tex.), and/or U.S. Biological (Swampscott,
Mass.).
[0315] C-reactive protein (CRP) is a protein found in the blood in
response to inflammation (an acute-phase protein). CRP is typically
produced by the liver and by fat cells (adipocytes). It is a member
of the pentraxin family of proteins. The human CRP polypeptide
sequence is set forth in, e.g., Genbank Accession No. NP_000558.
The human CRP mRNA (coding) sequence is set forth in, e.g., Genbank
Accession No. NM_000567. One skilled in the art will appreciate
that CRP is also known as PTX1, MGC88244, and MGC149895.
[0316] Serum amyloid A (SAA) proteins are a family of
apolipoproteins associated with high-density lipoprotein (HDL) in
plasma. Different isoforms of SAA are expressed constitutively
(constitutive SAAs) at different levels or in response to
inflammatory stimuli (acute phase SAAs). These proteins are
predominantly produced by the liver. The conservation of these
proteins throughout invertebrates and vertebrates suggests SAAs
play a highly essential role in all animals. Acute phase serum
amyloid A proteins (A-SAAs) are secreted during the acute phase of
inflammation. The human SAA polypeptide sequence is set forth in,
e.g., Genbank Accession No. NP_000322. The human SAA mRNA (coding)
sequence is set forth in, e.g., Genbank Accession No. NM_000331.
One skilled in the art will appreciate that SAA is also known as
PIG4, TP53I4, MGC111216, and SAA1.
[0317] 3. Cellular Adhesion Molecules (IgSF CAMs)
[0318] The determination of the presence or level of one or more
immunoglobulin superfamily cellular adhesion molecules in a sample
is also useful in the present invention. As used herein, the term
"immunoglobulin superfamily cellular adhesion molecule" (IgSF CAM)
includes any of a variety of polypeptides or proteins located on
the surface of a cell that have one or more immunoglobulin-like
fold domains, and which function in intercellular adhesion and/or
signal transduction. In many cases, IgSF CAMs are transmembrane
proteins. Non-limiting examples of IgSF CAMs include Neural Cell
Adhesion Molecules (NCAMs; e.g., NCAM-120, NCAM-125, NCAM-140,
NCAM-145, NCAM-180, NCAM-185, etc.), Intercellular Adhesion
Molecules (ICAMs, e.g., ICAM-1, ICAM-2, ICAM-3, ICAM-4, and
ICAM-5), Vascular Cell Adhesion Molecule-1 (VCAM-1),
Platelet-Endothelial Cell Adhesion Molecule-1 (PECAM-1), L1 Cell
Adhesion Molecule (L1CAM), cell adhesion molecule with homology to
L1CAM (close homolog of L1) (CHL1), sialic acid binding Ig-like
lectins (SIGLECs; e.g., SIGLEC-1, SIGLEC-2, SIGLEC-3, SIGLEC-4,
etc.), Nectins (e.g., Nectin-1, Nectin-2, Nectin-3, etc.), and
Nectin-like molecules (e.g., Necl-1, Necl-2, Necl-3, Necl-4, and
Necl-5). Preferably, the presence or level of ICAM-1 and/or VCAM-1
is determined.
[0319] ICAM-1 is a transmembrane cellular adhesion protein that is
continuously present in low concentrations in the membranes of
leukocytes and endothelial cells. Upon cytokine stimulation, the
concentrations greatly increase. ICAM-1 can be induced by IL-1 and
TNF.alpha. and is expressed by the vascular endothelium,
macrophages, and lymphocytes. In IBD, proinflammatory cytokines
cause inflammation by upregulating expression of adhesion molecules
such as ICAM-1 and VCAM-1. The increased expression of adhesion
molecules recruit more lymphocytes to the infected tissue,
resulting in tissue inflammation (see, Goke et al., J.
Gastroenterol., 32:480 (1997); and Rijcken et al., Gut, 51:529
(2002)). ICAM-1 is encoded by the intercellular adhesion molecule 1
gene (ICAM1; Entrez GeneID:3383; Genbank Accession No. NM_000201)
and is produced after processing of the intercellular adhesion
molecule 1 precursor polypeptide (Genbank Accession No.
NP_000192).
[0320] VCAM-1 is a transmembrane cellular adhesion protein that
mediates the adhesion of lymphocytes, monocytes, eosinophils, and
basophils to vascular endothelium. Upregulation of VCAM-1 in
endothelial cells by cytokines occurs as a result of increased gene
transcription (e.g., in response to Tumor necrosis factor-alpha
(TNF.alpha.) and Interleukin-1 (IL-1)). VCAM-1 is encoded by the
vascular cell adhesion molecule 1 gene (VCAM1; Entrez GeneID:7412)
and is produced after differential splicing of the transcript
(Genbank Accession No. NM_001078 (variant 1) or NM_080682 (variant
2)), and processing of the precursor polypeptide splice isoform
(Genbank Accession No. NP_001069 (isoform a) or NP_542413 (isoform
b)).
[0321] In certain instances, the presence or level of an IgSF CAM
is detected at the level of mRNA expression with an assay such as,
for example, a hybridization assay or an amplification-based assay.
In certain other instances, the presence or level of an IgSF CAM is
detected at the level of protein expression using, for example, an
immunoassay (e.g., ELISA) or an immunohistochemical assay. Suitable
antibodies and/or ELISA kits for determining the presence or level
of ICAM-1 and/or VCAM-1 in a sample such as a tissue sample,
biopsy, serum, plasma, saliva, urine, or stool are available from,
e.g., Invitrogen (Camarillo, Calif.), Santa Cruz Biotechnology,
Inc. (Santa Cruz, Calif.), and/or Abcam Inc. (Cambridge,
Mass.).
[0322] 4. S100 Proteins
[0323] The determination of the presence or level of at least one
S100 protein in a sample is also useful in the present invention.
As used herein, the term "S100 protein" includes any member of a
family of low molecular mass acidic proteins characterized by
cell-type-specific expression and the presence of 2 EF-hand
calcium-binding domains. There are at least 21 different types of
S100 proteins in humans. The name is derived from the fact that
S100 proteins are 100% soluble in ammonium sulfate at neutral pH.
Most S100 proteins are homodimeric, consisting of two identical
polypeptides held together by non-covalent bonds. Although S100
proteins are structurally similar to calmodulin, they differ in
that they are cell-specific, expressed in particular cells at
different levels depending on environmental factors. S-100 proteins
are normally present in cells derived from the neural crest (e.g.,
Schwann cells, melanocytes, glial cells), chondrocytes, adipocytes,
myoepithelial cells, macrophages, Langerhans cells, dendritic
cells, and keratinocytes. S100 proteins have been implicated in a
variety of intracellular and extracellular functions such as the
regulation of protein phosphorylation, transcription factors,
Ca.sup.2+ homeostasis, the dynamics of cytoskeleton constituents,
enzyme activities, cell growth and differentiation, and the
inflammatory response.
[0324] Calgranulin is an S100 protein that is expressed in multiple
cell types, including renal epithelial cells and neutrophils, and
are abundant in infiltrating monocytes and granulocytes under
conditions of chronic inflammation. Examples of calgranulins
include, without limitation, calgranulin A (also known as S100A8 or
MRP-8), calgranulin B (also known as S100A9 or MRP-14), and
calgranulin C (also known as S100A12).
[0325] In certain instances, the presence or level of a particular
S100 protein is detected at the level of mRNA expression with an
assay such as, for example, a hybridization assay or an
amplification-based assay. In certain other instances, the presence
or level of a particular S100 protein is detected at the level of
protein expression using, for example, an immunoassay (e.g., ELISA)
or an immunohistochemical assay. Suitable ELISA kits for
determining the presence or level of an S100 protein such as
calgranulin A (S100A8), calgranulin B (S100A9), or calgranulin C
(S100A12) in a serum, plasma, or urine sample are available from,
e.g., Peninsula Laboratories Inc. (San Carlos, Calif.) and Hycult
biotechnology b.v. (Uden, The Netherlands).
[0326] Calprotectin, the complex of S100A8 and S100A9, is a
calcium- and zinc-binding protein in the cytosol of neutrophils,
monocytes, and keratinocytes. Calprotectin is a major protein in
neutrophilic granulocytes and macrophages and accounts for as much
as 60% of the total protein in the cytosol fraction in these cells.
It is therefore a surrogate marker of neutrophil turnover. Its
concentration in stool correlates with the intensity of neutrophil
infiltration of the intestinal mucosa and with the severity of
inflammation. In some instances, calprotectin can be measured with
an ELISA using small (50-100 mg) fecal samples (see, e.g., Johne et
al., Scand J Gastroenterol., 36:291-296 (2001)).
[0327] 5. Other Inflammatory Markers
[0328] The determination of the presence or level of lactoferrin in
a sample is also useful in the present invention. In certain
instances, the presence or level of lactoferrin is detected at the
level of mRNA expression with an assay such as, for example, a
hybridization assay or an amplification-based assay. In certain
other instances, the presence or level of lactoferrin is detected
at the level of protein expression using, for example, an
immunoassay (e.g., ELISA) or an immunohistochemical assay. A
lactoferrin ELISA kit available from Calbiochem (San Diego, Calif.)
can be used to detect human lactoferrin in a plasma, urine,
bronchoalveolar lavage, or cerebrospinal fluid sample. Similarly,
an ELISA kit available from U.S. Biological (Swampscott, Mass.) can
be used to determine the level of lactoferrin in a plasma sample.
U.S. Patent Publication No. 20040137536 describes an ELISA assay
for determining the presence of elevated lactoferrin levels in a
stool sample. Likewise, U.S. Patent Publication No. 20040033537
describes an ELISA assay for determining the concentration of
endogenous lactoferrin in a stool, mucus, or bile sample. In some
embodiments, then presence or level of anti-lactoferrin antibodies
can be detected in a sample using, e.g., lactoferrin protein or a
fragment thereof.
[0329] The determination of the presence or level of one or more
pyruvate kinase isozymes such as M1-PK and M2-PK in a sample is
also useful in the present invention. In certain instances, the
presence or level of M1-PK and/or M2-PK is detected at the level of
mRNA expression with an assay such as, for example, a hybridization
assay or an amplification-based assay. In certain other instances,
the presence or level of M1-PK and/or M2-PK is detected at the
level of protein expression using, for example, an immunoassay
(e.g., ELISA) or an immunohistochemical assay. Pyruvate kinase
isozymes M1/M2 are also known as pyruvate kinase muscle isozyme
(PKM), pyruvate kinase type K, cytosolic thyroid hormone-binding
protein (CTHBP), thyroid hormone-binding protein 1 (THBP1), or
opa-interacting protein 3 (OIP3).
[0330] In further embodiments, the determination of the presence or
level of one or more growth factors in a sample is also useful in
the present invention. Non-limiting examples of growth factors
include transforming growth factors (TGF) such as TGF-.alpha.,
TGF-.beta., TGF-.beta.2, TGF-.beta.3, etc., which are described in
detail below.
[0331] 6. Exemplary Set of Inflammatory Markers
[0332] In particular embodiments, at least one or a plurality
(e.g., two, three, four, five, six, seven, or all eight, such as,
e.g., a panel or an array) of the following inflammatory markers
can be detected (e.g., alone or in combination with biomarkers from
other categories) to aid or assist in predicting disease course,
and/or to improve the accuracy of selecting therapy, optimizing
therapy, reducing toxicity, and/or monitoring the efficacy of
therapeutic treatment to anti-TNF drug therapy: (1) GM-CSF; (2)
IFN-.gamma.; (3) IL-1.beta.; (4) IL-2; (5) IL-6; (6) IL-8; (7)
TNF-.alpha.; and (8) sTNF RII.
[0333] C. Anti-Inflammatory Markers
[0334] In certain embodiments, a variety of anti-inflammatory
markers are particularly useful in the methods of the present
invention for personalized therapeutic management by selecting
therapy, optimizing therapy, reducing toxicity, and/or monitoring
the efficacy of therapeutic treatment with one or more therapeutic
agents such as biologics (e.g., anti-TNF drugs). In particular
embodiments, the methods described herein utilize the determination
of a disease activity profile (DAP) based upon one or more (a
plurality of) anti-inflammatory markers (e.g., alone or in
combination with biomarkers from other categories) to aid or assist
in predicting disease course, selecting an appropriate anti-TNF
drug therapy, optimizing anti-TNF drug therapy, reducing toxicity
associated with anti-TNF drug therapy, and/or monitoring the
efficacy of therapeutic treatment with an anti-TNF drug.
[0335] Non-limiting examples of anti-inflammatory markers include
IL-12p70 and IL-10. In preferred embodiments, the presence and/or
concentration levels of both IL-12p70 and IL-10 are determined.
[0336] In certain instances, the presence or level of a particular
anti-inflammatory marker is detected at the level of mRNA
expression with an assay such as, for example, a hybridization
assay or an amplification-based assay. In certain other instances,
the presence or level of a particular anti-inflammatory marker is
detected at the level of protein expression using, for example, an
immunoassay (e.g., ELISA) or an immunohistochemical assay.
[0337] The human IL-12p70 polypeptide is a heterodimer made up of
two subunits of IL-12 proteins: one is 40 kDa (IL-12p40) and one is
35 kDa (IL-12p35). Suitable ELISA kits for determining the presence
or level of IL-12p70 in a serum, plasma, saliva, or urine sample
are available from, e.g., Gen-Probe Diaclone SAS (France), Abazyme
(Needham, Mass.), BD Biosciences Pharmingen (San Diego, Calif.),
Cell Sciences (Canton, Mass.), eBioscience (San Diego, Calif.),
Invitrogen (Camarillo, Calif.), R&D Systems, Inc. (Minneapolis,
Minn.), and Thermo Scientific Pierce Protein Research Products
(Rockford, Ill.).
[0338] The human IL-10 polypeptide is an anti-inflammatory cytokine
that is also known as human cytokine synthesis inhibitory factor
(CSIF). Suitable ELISA kits for determining the presence or level
of IL-12p70 in a serum, plasma, saliva, or urine sample are
available from, e.g., Antigenix America Inc. (Huntington Station,
N.Y.), BD Biosciences Pharmingen (San Diego, Calif.), Cell Sciences
(Canton, Mass.), eBioscience (San Diego, Calif.), Gen-Probe
Diaclone SAS (France), Invitrogen (Camarillo, Calif.), R&D
Systems, Inc. (Minneapolis, Minn.), and Thermo Scientific Pierce
Protein Research Products (Rockford, Ill.).
[0339] D. Serology (Immune Markers)
[0340] The determination of serological or immune markers such as
autoantibodies in a sample (e.g., serum sample) is also useful in
the present invention. Antibodies against anti-inflammatory
molecules such as IL-10, TGF-.beta., and others might suppress the
body's ability to control inflammation and the presence or level of
these antibodies in the patient indicates the use of powerful
immunosuppressive medications such as anti-TNF drugs. Mucosal
healing might result in a decrease in the antibody titre of
antibodies to bacterial antigens such as, e.g., OmpC, flagellins
(cBir-1, Fla-A, Fla-X, etc.), 12, and others (pANCA, ASCA,
etc.).
[0341] As such, in certain aspects, the methods described herein
utilize the determination of a disease activity profile (DAP) based
upon one or more (a plurality of) serological or immune markers
(e.g., alone or in combination with biomarkers from other
categories) to aid or assist in predicting disease course,
selecting an appropriate anti-TNF drug therapy, optimizing anti-TNF
drug therapy, reducing toxicity associated with anti-TNF drug
therapy, and/or monitoring the efficacy of therapeutic treatment
with an anti-TNF drug.
[0342] Non-limiting examples of serological immune markers suitable
for use in the present invention include anti-neutrophil
antibodies, anti-Saccharomyces cerevisiae antibodies, and/or other
anti-microbial antibodies.
[0343] 1. Anti-Neutrophil Antibodies
[0344] The determination of ANCA levels and/or the presence or
absence of pANCA in a sample is useful in the methods of the
present invention. As used herein, the term "anti-neutrophil
cytoplasmic antibody" or "ANCA" includes antibodies directed to
cytoplasmic and/or nuclear components of neutrophils. ANCA activity
can be divided into several broad categories based upon the ANCA
staining pattern in neutrophils: (1) cytoplasmic neutrophil
staining without perinuclear highlighting (cANCA); (2) perinuclear
staining around the outside edge of the nucleus (pANCA); (3)
perinuclear staining around the inside edge of the nucleus (NSNA);
and (4) diffuse staining with speckling across the entire
neutrophil (SAPPA). In certain instances, pANCA staining is
sensitive to DNase treatment. The term ANCA encompasses all
varieties of anti-neutrophil reactivity, including, but not limited
to, cANCA, pANCA, NSNA, and SAPPA. Similarly, the term ANCA
encompasses all immunoglobulin isotypes including, without
limitation, immunoglobulin A and G.
[0345] ANCA levels in a sample from an individual can be
determined, for example, using an immunoassay such as an
enzyme-linked immunosorbent assay (ELISA) with alcohol-fixed
neutrophils. The presence or absence of a particular category of
ANCA such as pANCA can be determined, for example, using an
immunohistochemical assay such as an indirect fluorescent antibody
(IFA) assay. Preferably, the presence or absence of pANCA in a
sample is determined using an immunofluorescence assay with
DNase-treated, fixed neutrophils. In addition to fixed neutrophils,
antigens specific for ANCA that are suitable for determining ANCA
levels include, without limitation, unpurified or partially
purified neutrophil extracts; purified proteins, protein fragments,
or synthetic peptides such as histone H1 or ANCA-reactive fragments
thereof (see, e.g., U.S. Pat. No. 6,074,835); histone H1-like
antigens, porin antigens, Bacteroides antigens, or ANCA-reactive
fragments thereof (see, e.g., U.S. Pat. No. 6,033,864); secretory
vesicle antigens or ANCA-reactive fragments thereof (see, e.g.,
U.S. patent application Ser. No. 08/804,106); and anti-ANCA
idiotypic antibodies. One skilled in the art will appreciate that
the use of additional antigens specific for ANCA is within the
scope of the present invention.
[0346] 2. Anti-Saccharomyces cerevisiae Antibodies
[0347] The determination of ASCA (e.g., ASCA-IgA and/or ASCA-IgG)
levels in a sample is useful in the present invention. As used
herein, the term "anti-Saccharomyces cerevisiae immunoglobulin A"
or "ASCA-IgA" includes antibodies of the immunoglobulin A isotype
that react specifically with S. cerevisiae. Similarly, the term
"anti-Saccharomyces cerevisiae immunoglobulin G" or "ASCA-IgG"
includes antibodies of the immunoglobulin G isotype that react
specifically with S. cerevisiae.
[0348] The determination of whether a sample is positive for
ASCA-IgA or ASCA-IgG is made using an antigen specific for ASCA.
Such an antigen can be any antigen or mixture of antigens that is
bound specifically by ASCA-IgA and/or ASCA-IgG. Although ASCA
antibodies were initially characterized by their ability to bind S.
cerevisiae, those of skill in the art will understand that an
antigen that is bound specifically by ASCA can be obtained from S.
cerevisiae or from a variety of other sources so long as the
antigen is capable of binding specifically to ASCA antibodies.
Accordingly, exemplary sources of an antigen specific for ASCA,
which can be used to determine the levels of ASCA-IgA and/or
ASCA-IgG in a sample, include, without limitation, whole killed
yeast cells such as Saccharomyces or Candida cells; yeast cell wall
mannan such as phosphopeptidomannan (PPM); oligosachharides such as
oligomannosides; neoglycolipids; anti-ASCA idiotypic antibodies;
and the like. Different species and strains of yeast, such as S.
cerevisiae strain Su1, Su2, CBS 1315, or BM 156, or Candida
albicans strain VW32, are suitable for use as an antigen specific
for ASCA-IgA and/or ASCA-IgG. Purified and synthetic antigens
specific for ASCA are also suitable for use in determining the
levels of ASCA-IgA and/or ASCA-IgG in a sample. Examples of
purified antigens include, without limitation, purified
oligosaccharide antigens such as oligomannosides. Examples of
synthetic antigens include, without limitation, synthetic
oligomannosides such as those described in U.S. Patent Publication
No. 20030105060, e.g., D-Man .beta.(1-2) D-Man .beta.(1-2) D-Man
.beta.(1-2) D-Man-OR, D-Man .alpha.(1-2) D-Man .alpha.(1-2) D-Man
.alpha.(1-2) D-Man-OR, and D-Man .alpha.(1-3) D-Man .alpha.(1-2)
D-Man .alpha.(1-2) D-Man-OR, wherein R is a hydrogen atom, a
C.sub.1 to C.sub.20 alkyl, or an optionally labeled connector
group.
[0349] Preparations of yeast cell wall mannans, e.g., PPM, can be
used in determining the levels of ASCA-IgA and/or ASCA-IgG in a
sample. Such water-soluble surface antigens can be prepared by any
appropriate extraction technique known in the art, including, for
example, by autoclaving, or can be obtained commercially (see,
e.g., Lindberg et al., Gut, 33:909-913 (1992)). The acid-stable
fraction of PPM is also useful in the present invention (Sendid et
al., Clin. Diag. Lab. Immunol., 3:219-226 (1996)). An exemplary PPM
that is useful in determining ASCA levels in a sample is derived
from S. uvarum strain ATCC #38926.
[0350] Purified oligosaccharide antigens such as oligomannosides
can also be useful in determining the levels of ASCA-IgA and/or
ASCA-IgG in a sample. The purified oligomannoside antigens are
preferably converted into neoglycolipids as described in, for
example, Faille et al., Eur. J. Microbiol. Infect. Dis., 11:438-446
(1992). One skilled in the art understands that the reactivity of
such an oligomannoside antigen with ASCA can be optimized by
varying the mannosyl chain length (Frosh et al., Proc Natl. Acad.
Sci. USA, 82:1194-1198 (1985)); the anomeric configuration
(Fukazawa et al., In "Immunology of Fungal Disease," E. Kurstak
(ed.), Marcel Dekker Inc., New York, pp. 37-62 (1989); Nishikawa et
al., Microbiol. Immunol., 34:825-840 (1990); Poulain et al., Eur.
J. Clin. Microbiol., 23:46-52 (1993); Shibata et al., Arch.
Biochem. Biophys., 243:338-348 (1985); Trinel et al., Infect.
Immun., 60:3845-3851 (1992)); or the position of the linkage
(Kikuchi et al., Planta, 190:525-535 (1993)).
[0351] Suitable oligomannosides for use in the methods of the
present invention include, without limitation, an oligomannoside
having the mannotetraose Man(1-3) Man(1-2) Man(1-2) Man. Such an
oligomannoside can be purified from PPM as described in, e.g.,
Faille et al., supra. An exemplary neoglycolipid specific for ASCA
can be constructed by releasing the oligomannoside from its
respective PPM and subsequently coupling the released
oligomannoside to 4-hexadecylaniline or the like.
[0352] 3. Anti-Microbial Antibodies
[0353] The determination of anti-OmpC antibody levels in a sample
is also useful in the present invention. As used herein, the term
"anti-outer membrane protein C antibody" or "anti-OmpC antibody"
includes antibodies directed to a bacterial outer membrane porin as
described in, e.g., PCT Patent Publication No. WO 01/89361. The
term "outer membrane protein C" or "OmpC" refers to a bacterial
porin that is immunoreactive with an anti-OmpC antibody.
[0354] The level of anti-OmpC antibody present in a sample from an
individual can be determined using an OmpC protein or a fragment
thereof such as an immunoreactive fragment thereof. Suitable OmpC
antigens useful in determining anti-OmpC antibody levels in a
sample include, without limitation, an OmpC protein, an OmpC
polypeptide having substantially the same amino acid sequence as
the OmpC protein, or a fragment thereof such as an immunoreactive
fragment thereof. As used herein, an OmpC polypeptide generally
describes polypeptides having an amino acid sequence with greater
than about 50% identity, preferably greater than about 60%
identity, more preferably greater than about 70% identity, still
more preferably greater than about 80%, 85%, 90%, 95%, 96%, 97%,
98%, or 99% amino acid sequence identity with an OmpC protein, with
the amino acid identity determined using a sequence alignment
program such as CLUSTALW. Such antigens can be prepared, for
example, by purification from enteric bacteria such as E. coli, by
recombinant expression of a nucleic acid such as Genbank Accession
No. K00541, by synthetic means such as solution or solid phase
peptide synthesis, or by using phage display.
[0355] The determination of anti-I2 antibody levels in a sample is
also useful in the present invention. As used herein, the term
"anti-I2 antibody" includes antibodies directed to a microbial
antigen sharing homology to bacterial transcriptional regulators as
described in, e.g., U.S. Pat. No. 6,309,643. The term 12'' refers
to a microbial antigen that is immunoreactive with an anti-I2
antibody. The microbial I2 protein is a polypeptide of 100 amino
acids sharing some similarity weak homology with the predicted
protein 4 from C. pasteurianum, Rv3557c from Mycobacterium
tuberculosis, and a transcriptional regulator from Aquifex
aeolicus. The nucleic acid and protein sequences for the I2 protein
are described in, e.g., U.S. Pat. No. 6,309,643.
[0356] The level of anti-I2 antibody present in a sample from an
individual can be determined using an I2 protein or a fragment
thereof such as an immunoreactive fragment thereof. Suitable I2
antigens useful in determining anti-I2 antibody levels in a sample
include, without limitation, an I2 protein, an I2 polypeptide
having substantially the same amino acid sequence as the I2
protein, or a fragment thereof such as an immunoreactive fragment
thereof. Such I2 polypeptides exhibit greater sequence similarity
to the I2 protein than to the C. pasteurianum protein 4 and include
isotype variants and homologs thereof. As used herein, an I2
polypeptide generally describes polypeptides having an amino acid
sequence with greater than about 50% identity, preferably greater
than about 60% identity, more preferably greater than about 70%
identity, still more preferably greater than about 80%, 85%, 90%,
95%, 96%, 97%, 98%, or 99% amino acid sequence identity with a
naturally-occurring I2 protein, with the amino acid identity
determined using a sequence alignment program such as CLUSTALW.
Such I2 antigens can be prepared, for example, by purification from
microbes, by recombinant expression of a nucleic acid encoding an
I2 antigen, by synthetic means such as solution or solid phase
peptide synthesis, or by using phage display.
[0357] The determination of anti-flagellin antibody levels in a
sample is also useful in the present invention. As used herein, the
term "anti-flagellin antibody" includes antibodies directed to a
protein component of bacterial flagella as described in, e.g., PCT
Patent Publication No. WO 03/053220 and U.S. Patent Publication No.
20040043931. The term "flagellin" refers to a bacterial flagellum
protein that is immunoreactive with an anti-flagellin antibody.
Microbial flagellins are proteins found in bacterial flagellum that
arrange themselves in a hollow cylinder to form the filament.
[0358] The level of anti-flagellin antibody present in a sample
from an individual can be determined using a flagellin protein or a
fragment thereof such as an immunoreactive fragment thereof.
Suitable flagellin antigens useful in determining anti-flagellin
antibody levels in a sample include, without limitation, a
flagellin protein such as Cbir-1 flagellin, flagellin X, flagellin
A, flagellin B, fragments thereof, and combinations thereof, a
flagellin polypeptide having substantially the same amino acid
sequence as the flagellin protein, or a fragment thereof such as an
immunoreactive fragment thereof. As used herein, a flagellin
polypeptide generally describes polypeptides having an amino acid
sequence with greater than about 50% identity, preferably greater
than about 60% identity, more preferably greater than about 70%
identity, still more preferably greater than about 80%, 85%, 90%,
95%, 96%, 97%, 98%, or 99% amino acid sequence identity with a
naturally-occurring flagellin protein, with the amino acid identity
determined using a sequence alignment program such as CLUSTALW.
Such flagellin antigens can be prepared, e.g., by purification from
bacterium such as Helicobacter Bilis, Helicobacter mustelae,
Helicobacter pylori, Butyrivibrio fibrisolvens, and bacterium found
in the cecum, by recombinant expression of a nucleic acid encoding
a flagellin antigen, by synthetic means such as solution or solid
phase peptide synthesis, or by using phage display.
[0359] E. Cell Surface Receptors
[0360] The determination of cell surface receptors in a sample is
also useful in the present invention. The half-life of anti-TNF
drugs such as Remicade and Humira is significantly decreased in
patients with a high level of inflammation. CD64, the high-affinity
receptor for immunoglobulin (Ig) G1 and IgG3, is predominantly
expressed by mononuclear phagocytes. Resting polymorphonuclear
(PMN) cells scarcely express CD64, but the expression of this
marker is unregulated by interferon and
granulocyte-colony-stimulating factor acting on myeloid precursors
in the bone marrow. Crosslinking of CD64 with IgG complexes exerts
a number of cellular responses, including the internalization of
immune complexes by endocytosis, phagocytosis of opsonized
particles, degranulation, activation of the oxidative burst, and
the release of cytokines.
[0361] As such, in certain aspects, the methods described herein
utilize the determination of a disease activity profile (DAP) based
upon one or more (a plurality of) cell surface receptors such as
CD64 (e.g., alone or in combination with biomarkers from other
categories) to aid or assist in predicting disease course,
selecting an appropriate anti-TNF drug therapy, optimizing anti-TNF
drug therapy, reducing toxicity associated with anti-TNF drug
therapy, and/or monitoring the efficacy of therapeutic treatment
with an anti-TNF drug.
[0362] F. Signaling Pathways
[0363] The determination of signaling pathways in a sample is also
useful in the present invention. Polymorphonuclear (PMN) cell
activation, followed by infltration into the intestinal mucosa
(synovium for RA) and migration across the crypt epithelium is
regarded as a key feature of IBD. It has been estimated by fecal
indium-111-labeled leukocyte excretion that migration of PMN cells
from the circulation to the diseased section of the intestine is
increased by 10-fold or more in IBD patients. Thus, measuring
activation of PMN cells from blood or tissue inflammation by
measuring signaling pathways using an assay such as the
Collaborative Enzyme Enhanced Reactive ImmunoAssay (CEER) described
herein is an ideal way to understand inflammatory disease.
[0364] As such, in certain aspects, the methods described herein
utilize the determination of a disease activity profile (DAP) based
upon one or more (a plurality of) signal transduction molecules in
one or more signaling pathways (e.g., alone or in combination with
biomarkers from other categories) to aid or assist in predicting
disease course, selecting an appropriate anti-TNF drug therapy,
optimizing anti-TNF drug therapy, reducing toxicity associated with
anti-TNF drug therapy, and/or monitoring the efficacy of
therapeutic treatment with an anti-TNF drug. In preferred
embodiments, the total (e.g., expression) level and/or activation
(e.g., phosphorylation) level of one or more signal transduction
molecules in one or more signaling pathways is measured.
[0365] The term "signal transduction molecule" or "signal
transducer" includes proteins and other molecules that carry out
the process by which a cell converts an extracellular signal or
stimulus into a response, typically involving ordered sequences of
biochemical reactions inside the cell. Examples of signal
transduction molecules include, but are not limited to, receptor
tyrosine kinases such as EGFR (e.g., EGFR/HER1/ErbB1,
HER2/Neu/ErbB2, HER3/ErbB3, HER4/ErbB4), VEGFR1/FLT1,
VEGFR2/FLK1/KDR, VEGFR3/FLT4, FLT3/FLK2, PDGFR (e.g., PDGFRA,
PDGFRB), c-KIT/SCFR, INSR (insulin receptor), IGF-IR, IGF-IIR, IRR
(insulin receptor-related receptor), CSF-1R, FGFR 1-4, HGFR 1-2,
CCK4, TRK A-C, c-MET, RON, EPHA 1-8, EPHB 1-6, AXL, MER, TYRO3, TIE
1-2, TEK, RYK, DDR 1-2, RET, c-ROS, V-cadherin, LTK (leukocyte
tyrosine kinase), ALK (anaplastic lymphoma kinase), ROR 1-2, MUSK,
AATYK 1-3, and RTK 106; truncated forms of receptor tyrosine
kinases such as truncated HER2 receptors with missing
amino-terminal extracellular domains (e.g., p95ErbB2 (p95m), p110,
p95c, p95n, etc.), truncated cMET receptors with missing
amino-terminal extracellular domains, and truncated HER3 receptors
with missing amino-terminal extracellular domains; receptor
tyrosine kinase dimers (e.g., p95HER2/HER3; p95HER2/HER2; truncated
HER3 receptor with HER1, HER2, HER3, or HER4; HER2/HER2; HER3/HER3;
HER2/HER3; HER1/HER2; HER1/HER3; HER2/HER4; HER3/HER4; etc.);
non-receptor tyrosine kinases such as BCR-ABL, Src, Frk, Btk, Csk,
Abl, Zap70, Fes/Fps, Fak, Jak, Ack, and LIMK; tyrosine kinase
signaling cascade components such as AKT (e.g., AKT1, AKT2, AKT3),
MEK (MAP2K1), ERK2 (MAPK1), ERK1 (MAPK3), PI3K (e.g., PIK3CA
(p110), PIK3R1 (p85)), PDK1, PDK2, phosphatase and tensin homolog
(PTEN), SGK3, 4E-BP1, P70S6K (e.g., p70 S6 kinase splice variant
alpha I), protein tyrosine phosphatases (e.g., PTP1B, PTPN13, BDP1,
etc.), RAF, PLA2, MEKK, JNKK, JNK, p38, Shc (p66), Ras (e.g.,
K-Ras, N-Ras, H-Ras), Rho, Racl, Cdc42, PLC, PKC, p53, cyclin D1,
STAT1, STAT3, phosphatidylinositol 4,5-bisphosphate (PIP2),
phosphatidylinositol 3,4,5-trisphosphate (PIP3), mTOR, BAD, p21,
p27, ROCK, IP3, TSP-1, NOS, GSK-3.beta., RSK 1-3, JNK, c-Jun, Rb,
CREB, Ki67, paxillin, NF-kB, and IKK; nuclear hormone receptors
such as estrogen receptor (ER), progesterone receptor (PR),
androgen receptor, glucocorticoid receptor, mineralocorticoid
receptor, vitamin A receptor, vitamin D receptor, retinoid
receptor, thyroid hormone receptor, and orphan receptors; nuclear
receptor coactivators and repressors such as amplified in breast
cancer-1 (AIB1) and nuclear receptor corepressor 1 (NCOR),
respectively; and combinations thereof
[0366] The term "activation state" refers to whether a particular
signal transduction molecule is activated. Similarly, the term
"activation level" refers to what extent a particular signal
transduction molecule is activated. The activation state typically
corresponds to the phosphorylation, ubiquitination, and/or
complexation status of one or more signal transduction molecules.
Non-limiting examples of activation states (listed in parentheses)
include: HER1/EGFR (EGFRvIII, phosphorylated (p-) EGFR, EGFR:Shc,
ubiquitinated (u-) EGFR, p-EGFRvIII); ErbB2 (p-ErbB2, p95HER2
(truncated ErbB2), p-p95HER2, ErbB2:Shc, ErbB2:PI3K, ErbB2:EGFR,
ErbB2:ErbB3, ErbB2:ErbB4); ErbB3 (p-ErbB3, truncated ErbB3,
ErbB3:PI3K, p-ErbB3:PI3K, ErbB3:Shc); ErbB4 (p-ErbB4, ErbB4:Shc);
c-MET (p-c-MET, truncated c-MET, c-Met:HGF complex); AKT1 (p-AKT1);
AKT2 (p-AKT2); AKT3 (p-AKT3); PTEN (p-PTEN); P70S6K (p-P70S6K); MEK
(p-MEK); ERK1 (p-ERK1); ERK2 (p-ERK2); PDK1 (p-PDK1); PDK2
(p-PDK2); SGK3 (p-SGK3); 4E-BP1 (p-4E-BP1); PIK3R1 (p-PIK3R1);
c-KIT (p-c-KIT); ER (p-ER); IGF-1R (p-IGF-1R, IGF-1R:IRS, IRS:PI3K,
p-IRS, IGF-1R:PI3K); INSR (p-INSR); FLT3 (p-FLT3); HGFR1 (p-HGFR1);
HGFR2 (p-HGFR2); RET (p-RET); PDGFRA (p-PDGFRA); PDGFRB (p-PDGFRB);
VEGFR1 (p-VEGFR1, VEGFR1:PLCy, VEGFR1:Src); VEGFR2 (p-VEGFR2,
VEGFR2:PLC.gamma., VEGFR2:Src, VEGFR2:heparin sulphate,
VEGFR2:VE-cadherin); VEGFR3 (p-VEGFR3); FGFR1 (p-FGFR1); FGFR2
(p-FGFR2); FGFR3 (p-FGFR3); FGFR4 (p-FGFR4); TIEl (p-TIE1); TIE2
(p-TIE2); EPHA (p-EPHA); EPHB (p-EPHB); GSK-3.beta.
(p-GSK-3.beta.); NF-kB (p-NF-kB, NF-kB-IkB alpha complex and
others), IkB (p-IkB, p-P65:IkB); IKK (phospho IKK); BAD (p-BAD,
BAD:14-3-3); mTOR (p-mTOR); Rsk-1 (p-Rsk-1); Jnk (p-Jnk); P38
(p-P38); STAT1 (p-STAT1); STAT3 (p-STAT3); FAK (p-FAK); RB (p-RB);
Ki67; p53 (p-p53); CREB (p-CREB); c-Jun (p-c-Jim); c-Src (p-c-Src);
paxillin (p-paxillin); GRB2 (p-GRB2), Shc (p-Shc), Ras (p-Ras),
GAB1 (p-GAB1), SHP2 (p-SHP2), GRB2 (p-GRB2), CRKL (p-CRKL), PLCy
(p-PLCy), PKC (e.g., p-PKC.alpha., p-PKC.beta., p-PKC.delta.),
adducin (p-adducin), RB1 (p-RB1), and PYK2 (p-PYK2).
[0367] The following tables provide additional examples of signal
transduction molecules for which total levels and/or activation
(e.g., phosphorylation) levels can be determined in a sample (e.g.,
alone or in combination with biomarkers from other categories) to
aid or assist in predicting disease course, selecting an
appropriate anti-TNF drug therapy, optimizing anti-TNF drug
therapy, reducing toxicity associated with anti-TNF drug therapy,
or monitoring the efficacy of therapeutic treatment with an
anti-TNF drug.
TABLE-US-00002 Total/Phospho Assays Phospho Sites VEGFR2 Total
VEGFR2 Phospho Y951, 1212 Erk Total Erk Phospho T202/Y204 Akt Total
Akt Phospho T308, S473 MEK Total MEK Phospho S217/221 MEK Total MEK
Phospho S217/221 P70S6K Total P70S6k Phospho T389(T229) PTEN Total
VEGFR1(T) VEGFR1 Phospho SGK total SGK phosphor T320, S486 CRKL
Total CRKL Phospho Y207 SRC Total SRK Phospho Y 416, 527 FAK Total
FAK Phospho Y397 BCR Total BCR Phospho PI3K Activated PI3K
complexed P85 Y688 4EBP1 4EBP1 phospho T70, T37, T46 PRAS40 PRAS40
phospho T246
TABLE-US-00003 Phospho Total/Phospho Assays Sites TIE Total TIE-2
Phospho Y992 (S1119) Jak 2 Total JAK 2 Phospho Y1007/1008 STAT 5
Total STAT 5 Phospho Y694/699 STAT 3 Total STAT 3 Phospho Y705
FGFR1 total FGFR1 Phospho Y 653, 766 FGFR2 total FGFR 2 Phospho
Y653 FGFR3 total FGFR 3 Phospho FGFR4 total FGFR 4 Phospho Axl
total Axl Phospho Y702 BAD total BAD Phospho (S112)(S136) RSK total
RSK Phospho (T359/S363) PDK total PDK 1 Phospho (S241) JAK 1 and 3
total JAK 1 and 3 Phospho TSC2 total TSC 2 Phospho S664, S939 S6RP
Total S6RP Phospho S235/236
[0368] The Collaborative Enzyme Enhanced Reactive ImmunoAssay
(CEER), also known as the Collaborative Proximity Immunoassay
(COPIA), is described in the following patent documents which are
herein incorporated by reference in their entirety for all
purposes: PCT Publication No. WO 2008/036802; PCT Publication No.
WO 2009/012140; PCT Publication No. WO 2009/108637; PCT Publication
No. WO 2010/132723; PCT Publication No. WO 2011/008990; and PCT
Application No. PCT/US2010/053386, filed October 20, 2010.
[0369] G. Elimination Rate Constant
[0370] In certain embodiments, a marker for the disease activity
profile (DAP) is kel, or the elimination rate constant of an
antibody such as an anti-TNF antibody (e.g., infliximab). The
determination of an elimination rate constant such as kel is
particularly useful in the methods of the invention for
personalized therapeutic management by selecting therapy,
optimizing therapy, reducing toxicity, and/or monitoring the
efficacy of therapeutic treatment with one or more therapeutic
agents such as biologics (e.g., anti-TNF drugs).
[0371] In certain instances, a differential equation can be used to
model drug elimination from the patient. In certain instances, a
two-compartment PK model can be used. In this instance, the
equation for the drug in the central compartment following
intravenous bolus administration is:
dX 1 dt = - kel X 1 - k 12 X 1 + k 21 X 2. ##EQU00001##
[0372] The kelX1 term describes elimination of the drug from the
central compartment, while the k12X1 and k21X2 terms describe the
distribution of drug between the central and peripheral
compartments.
[0373] H. Genetic Markers
[0374] The determination of the presence or absence of allelic
variants (e.g., SNPs) in one or more genetic markers in a sample
(e.g., alone or in combination with biomarkers from other
categories) is also useful in the methods of the present invention
to aid or assist in predicting disease course, selecting an
appropriate anti-TNF drug therapy, optimizing anti-TNF drug
therapy, reducing toxicity associated with anti-TNF drug therapy,
or monitoring the efficacy of therapeutic treatment with an
anti-TNF drug.
[0375] Non-limiting examples of genetic markers include, but are
not limited to, any of the inflammatory pathway genes and
corresponding SNPs that can be genotyped as set forth in Table 1
(e.g., a NOD2/CARD15 gene, an IL12/IL23 pathway gene, etc.).
Preferably, the presence or absence of at least one allelic
variant, e.g., a single nucleotide polymorphism (SNP), in the
NOD2/CARD15 gene and/or one or more genes in the IL12/IL23 pathway
is determined. See, e.g., Barrett et al., Nat. Genet., 40:955-62
(2008) and Wang et al., Amer. J. Hum. Genet., 84:399-405
(2009).
TABLE-US-00004 TABLE 1 Gene SNP NOD2 (R702W) - SNP8 rs2066844 NOD2
(G908R) - SNP12 rs2066845 NOD2 (3020insC) - SNP13 rs5743293 ATG16L1
(T300A) rs2241880 IL23R (R381Q) rs11209026 DLG5 rs2165047
NOD2/CARD15 rs2066847 IL23R rs11465804 ATG16L1 rs3828309 MST1
rs3197999 PTGER4 rs4613763 IRGM rs11747270 TNFSF15 rs4263839 ZNF365
rs10995271 NKX2-3 rs11190140 PTPN2 rs2542151 PTPN22 rs2476601 ITLN1
rs2274910 IL12B rs10045431 CDKAL1 rs6908425 CCR6 rs2301436 JAK2
rs10758669 C11orf30 rs7927894 LRRK2, MUC19 rs11175593 ORMDL3
rs2872507 STAT3 rs744166 ICOSLG rs762421 GCKR rs780094 BTNL2,
SLC26A3, HLA-DRB1, rs3763313 HLA-DQA1 PUS10 rs13003464 CCL2, CCL7
rs991804 LYRM4 rs12529198 SLC22A23 rs17309827 IL18RAP rs917997
IL12RB2 rs7546245 IL12RB1 rs374326 CD3D rs3212262 CD3G rs3212262
CD247 rs704853 JUN rs6661505 CD3E rs7937334 IL18R1 rs1035127 CCR5
MAPK14 rs2237093 IL18 rs11214108 IFNG rs10878698 MAP2K6 rs2905443
STAT4 rs1584945 IL12A rs6800657 TYK2 rs12720356 ETV5 rs9867846
MAPK8 rs17697885 IRGM rs13361189 IRGM rs4958847 IRGM rs1000113 IRGM
rs11747270 TL1A/TNFSF15 rs6478109 TL1A/TNFSF15 rs6478108
TL1A/TNFSF15 rs4263839 PTN22 rs2476601 CCR6 rs1456893 CCR6
rs2301436 5p13/PTGER4 rs1373692 5p13/PTGER4 rs4495224 5p13/PTGER4
rs7720838 5p13/PTGER4 rs4613763 ITLN1 rs2274910 ITLN1 rs9286879
ITLN1 rs11584383 IBD5/5q31 rs2188962 IBD5/5q31 rs252057 IBD5/5q31
rs10067603 GCKR rs780094 TNFRSF6B rs1736135 ZNF365 rs224136 ZNF365
rs10995271 C11orf30 rs7927894 LRRK2; MUC19 rs1175593 IL-27
rs8049439 TLR2 rs4696480 TLR2 rs3804099 TLR2 rs3804100 TLR2
rs5743704 TLR2 rs2405432 TLR4 (D299G) rs4986790 TLR4 (T399I)
rs4986791 TLR4 (S360N) rs4987233 TLR9 rs187084 TLR9 rs352140 NFC4
rs4821544 KIF21B rs11584383 IKZF1 rs1456893 C11orf30 rs7927894
CCL2, CCL7 rs991804 ICOSLG rs762421 TNFAIP3 rs7753394 FLJ45139
rs2836754 PTGER4 rs4613763 ECM1 rs7511649 ECM1 (T130M) rs3737240
ECM1 (G290S) rs13294 GLI1 (G933D) rs2228224 GLI1 (Q1100E) rs2228226
MDR1 (3435C > T) rs1045642 MDR1 (A893S/T) rs2032582 MAGI2
rs6962966 MAGI2 rs2160322 IL26 rs12815372 IFNG, IL26 rs1558744
IFNG, IL26 rs971545 IL26 rs2870946 ARPC2 rs12612347 IL10, IL19
rs3024493 IL10, IL19 rs3024505 IL23R rs1004819 IL23R rs2201841
IL23R rs11465804 IL23R rs10889677 BTLN2 rs9268480 HLA-DRB1 rs660895
MEP1 rs6920863 MEP1 rs2274658 MEP1 rs4714952 MEP1 rs1059276 PUS10
rs13003464 PUS10 rs6706689 RNF186 rs3806308 RNF186 rs1317209 RNF186
rs6426833 FCGR2A, C rs10800309 CEP72 rs4957048 DLD, LAMB1 rs4598195
CAPN10, KIF1A rs4676410 IL23R rs11805303 IL23R rs7517847 IL12B/p40
rs1368438 IL12B/p40 rs10045431 IL12B/p40 rs6556416 IL12B/p40
rs6887695 IL12B/p40 rs3212227 STAT3 rs744166 JAK2 rs10974914 JAK2
rs10758669 NKX2-3 rs6584283 NKX2-3 rs10883365 NKX2-3 rs11190140
IL18RAP rs917997 LYRM4 rs12529198 CDKAL1 rs6908425 MAGI2 rs2160322
TNFRSF6B rs2160322 TNFRSF6B rs2315008 TNFRSF6B rs4809330 PSMG1
rs2094871 PSMG1 rs2836878 PTPN2 rs2542151 MST1/3p21 rs9858542
MST1/3p21 rs3197999 SLC22A23 rs17309827 MHC rs660895 XBP1
rs35873774 ICOSLG1 rs762421 BTLN2 rs3763313 BTLN2 rs2395185 BTLN2
rs9268480 ATG5 rs7746082 CUL2, CREM rs17582416 CARD9 rs4077515
ORMDL3 rs2872507 ORMDL3 rs2305480
[0376] Additional SNPs useful in the present invention include,
e.g., rs2188962, rs9286879, rs11584383, rs7746082, rs1456893,
rs1551398, rs17582416, rs3764147, rs1736135, rs4807569, rs7758080,
and rs8098673. See, e.g., Barrett et al., Nat. Genet., 40:955-62
(2008).
[0377] In particular embodiments, the presence or absence of one or
more mutations in one or more of the following genetic markers is
determined: inflammatory pathway genes, e.g., the presence or
absence of variant alleles (e.g., SNPs) in one or more inflammatory
markers such as, e.g., NOD2/CARD15 (e.g., SNP 8, SNP 12, and/or SNP
13 described in U.S. Pat. No. 7,592,437), ATG16L1 (e.g., the
rs2241880 (T300A) SNP described in Lakatos et al., Digestive and
Liver Disease, 40 (2008) 867-873), IL23R (e.g., the rs11209026
(R381Q) SNP described in Lakatos et al.), the human leukocyte
antigen (HLA) genes and/or cytokine genes described in, e.g.,
Gasche et al. (Eur. J. Gastroenterology & Hepatology, (2003)
15:599-606), and the DLGS and/or OCTN genes from the IBD5
locus.
[0378] 1. NOD2/CARD15
[0379] The determination of the presence or absence of allelic
variants such as SNPs in the NOD2/CARD15 gene is particularly
useful in the present invention. As used herein, the term
"NOD2/CARD15 variant" or "NOD2 variant" includes a nucleotide
sequence of a NOD2 gene containing one or more changes as compared
to the wild-type NOD2 gene or an amino acid sequence of a NOD2
polypeptide containing one or more changes as compared to the
wild-type NOD2 polypeptide sequence. NOD2, also known as CARD15,
has been localized to the IBD1 locus on chromosome 16 and
identified by positional-cloning (Hugot et al., Nature, 411:599-603
(2001)) as well as a positional candidate gene strategy (Ogura et
al., Nature, 411:603-606 (2001); Hampe et al., Lancet,
357:1925-1928 (2001)). The IBD1 locus has a high multipoint linkage
score (MLS) for inflammatory bowel disease (MLS=5.7 at marker
D16S411 in 16q12). See, e.g., Cho et al., Inflamm. Bowel Dis.,
3:186-190 (1997); Akolkar et al., Am. J. Gastroenterol.,
96:1127-1132 (2001); Ohmen et al., Hum. Mol. Genet., 5:1679-1683
(1996); Parkes et al., Lancet, 348:1588 (1996); Cavanaugh et al.,
Ann. Hum. Genet., 62:291-8 (1998); Brant et al., Gastroenterology,
115:1056-1061 (1998); Curran et al., Gastroenterology,
115:1066-1071 (1998); Hampe et al., Am. J. Hum. Genet., 64:808-816
(1999); and Annese et al., Eur. J. Hum. Genet., 7:567-573
(1999).
[0380] The mRNA (coding) and polypeptide sequences of human NOD2
are set forth in, e.g., Genbank Accession Nos. NM.sub.13 022162 and
NP_071445, respectively. In addition, the complete sequence of
human chromosome 16 clone RP11-327F22, which includes NOD2, is set
forth in, e.g., Genbank Accession No. AC007728. Furthermore, the
sequence of NOD2 from other species can be found in the GenBank
database.
[0381] The NOD2 protein contains amino-terminal caspase recruitment
domains (CARDs), which can activate NF-kappa B (NF-kB), and several
carboxy-terminal leucine-rich repeat domains (Ogura et al., J.
Biol. Chem., 276:4812-4818 (2001)). NOD2 has structural homology
with the apoptosis regulator Apaf-1/CED-4 and a class of plant
disease resistant gene products (Ogura et al., supra). Similar to
plant disease resistant gene products, NOD2 has an amino-terminal
effector domain, a nucleotide-binding domain and leucine rich
repeats (LRRs). Wild-type NOD2 activates nuclear factor NF-kappa B,
making it responsive to bacterial lipopolysaccharides (LPS; Ogura
et al., supra; Inohara et al., J. Biol. Chem., 276:2551-2554
(2001). NOD2 can function as an intercellular receptor for LPS,
with the leucine rich repeats required for responsiveness.
[0382] Variations at three single nucleotide polymorphisms in the
coding region of NOD2 have been previously described. These three
SNPs, designated R702W ("SNP 8"), G908R ("SNP 12"), and 1007fs
("SNP 13"), are located in the carboxy-terminal region of the NOD2
gene (Hugot et al., supra). A further description of SNP 8, SNP 12,
and SNP 13, as well as additional SNPs in the NOD2 gene suitable
for use in the invention, can be found in, e.g., U.S. Pat. Nos.
6,835,815; 6,858,391; and 7,592,437; and U.S. Patent Publication
Nos. 20030190639, 20050054021, and 20070072180.
[0383] In some embodiments, a NOD2 variant is located in a coding
region of the NOD2 locus, for example, within a region encoding
several leucine-rich repeats in the carboxy-terminal portion of the
NOD2 polypeptide. Such NOD2 variants located in the leucine-rich
repeat region of NOD2 include, without limitation, R702W ("SNP 8")
and G908R ("SNP 12"). A NOD2 variant useful in the invention can
also encode a NOD2 polypeptide with reduced ability to activate
NF-kappa B as compared to NF-kappa B activation by a wild-type NOD2
polypeptide. As a non-limiting example, the NOD2 variant 1007fs
("SNP 13") results in a truncated NOD2 polypeptide which has
reduced ability to induce NF-kappa B in response to LPS stimulation
(Ogura et al., Nature, 411:603-606 (2001)).
[0384] A NOD2 variant useful in the invention can be, for example,
R702W, G908R, or 1007fs. R702W, G908R, and 1007fs are located
within the coding region of NOD2. In one embodiment, a method of
the invention is practiced with the R702W NOD2 variant. As used
herein, the term "R702W" includes a single nucleotide polymorphism
within exon 4 of the NOD2 gene, which occurs within a triplet
encoding amino acid 702 of the NOD2 protein. The wild-type NOD2
allele contains a cytosine (c) residue at position 138,991 of the
AC007728 sequence, which occurs within a triplet encoding an
arginine at amino acid702. The R702W NOD2 variant contains a
thymine (t) residue at position 138,991 of the AC007728 sequence,
resulting in an arginine (R) to tryptophan (W) substitution at
amino acid 702 of the NOD2 protein. Accordingly, this NOD2 variant
is denoted "R702W" or "702W" and can also be denoted "R675W" based
on the earlier numbering system of Hugot et al., supra. In
addition, the R702W variant is also known as the "SNP 8" allele or
a "2" allele at SNP 8. The NCBI SNP ID number for R702W or SNP 8 is
rs2066844. The presence of the R702W NOD2 variant and other NOD2
variants can be conveniently detected, for example, by allelic
discrimination assays or sequence analysis.
[0385] A method of the invention can also be practiced with the
G908R NOD2 variant. As used herein, the term "G908R" includes a
single nucleotide polymorphism within exon 8 of the NOD2 gene,
which occurs within a triplet encoding amino acid 908 of the NOD2
protein. Amino acid 908 is located within the leucine rich repeat
region of the NOD2 gene. The wild-type NOD2 allele contains a
guanine (g) residue at position 128,377 of the AC007728 sequence,
which occurs within a triplet encoding glycine at amino acid 908.
The G908R NOD2 variant contains a cytosine (c) residue at position
128,377 of the AC007728 sequence, resulting in a glycine (G) to
arginine (R) substitution at amino acid 908 of the NOD2 protein.
Accordingly, this NOD2 variant is denoted "G908R" or "908R" and can
also be denoted "G881R" based on the earlier numbering system of
Hugot et al., supra. In addition, the G908R variant is also known
as the "SNP 12" allele or a "2" allele at SNP 12. The NCBI SNP ID
number for G908R SNP 12 is rs2066845.
[0386] A method of the invention can also be practiced with the
1007fs NOD2 variant. This variant is an insertion of a single
nucleotide that results in a frame shift in the tenth leucine-rich
repeat of the NOD2 protein and is followed by a premature stop
codon. The resulting truncation of the NOD2 protein appears to
prevent activation of NF-kappaB in response to bacterial
lipopolysaccharides (Ogura et al., supra). As used herein, the term
"1007fs" includes a single nucleotide polymorphism within exon 11
of the NOD2 gene, which occurs in a triplet encoding amino acid
1007 of the NOD2 protein. The 1007fs variant contains a cytosine
which has been added at position 121,139 of the AC007728 sequence,
resulting in a frame shift mutation at amino acid 1007.
Accordingly, this NOD2 variant is denoted "1007fs" and can also be
denoted "3020insC" or "980fs" based on the earlier numbering system
of Hugot et al., supra. In addition, the 1007fs NOD2 variant is
also known as the "SNP 13" allele or a "2" allele at SNP 13. The
NCBI SNP ID number for 1007fs or SNP 13 is rs2066847.
[0387] One skilled in the art recognizes that a particular NOD2
variant allele or other polymorphic allele can be conveniently
defined, for example, in comparison to a Centre d'Etude du
Polymorphisme Humain (CEPH) reference individual such as the
individual designated 1347-02 (Dib et al., Nature, 380:152-154
(1996)), using commercially available reference DNA obtained, for
example, from PE Biosystems (Foster City, Calif.). In addition,
specific information on SNPs can be obtained from the dbSNP of the
National Center for Biotechnology Information (NCBI).
[0388] A NOD2 variant can also be located in a non-coding region of
the NOD2 locus. Non-coding regions include, for example, intron
sequences as well as 5' and 3' untranslated sequences. A
non-limiting example of a NOD2 variant allele located in a
non-coding region of the NOD2 gene is the JW1 variant, which is
described in Sugimura et al., Am. J. Hum. Genet., 72:509-518 (2003)
and U.S. Patent Publication No. 20070072180. Examples of NOD2
variant alleles located in the 3' untranslated region of the NOD2
gene include, without limitation, the JW15 and JW16 variant
alleles, which are described in U.S. Patent Publication No.
20070072180. Examples of NOD2 variant alleles located in the 5'
untranslated region (e.g., promoter region) of the NOD2 gene
include, without limitation, the JW17 and JW18 variant alleles,
which are described in U.S. Patent Publication No. 20070072180.
[0389] As used herein, the term "JW1 variant allele" includes a
genetic variation at nucleotide 158 of intervening sequence 8
(intron 8) of the NOD2 gene. In relation to the AC007728 sequence,
the JW1 variant allele is located at position 128,143. The genetic
variation at nucleotide 158 of intron 8 can be, but is not limited
to, a single nucleotide substitution, multiple nucleotide
substitutions, or a deletion or insertion of one or more
nucleotides. The wild-type sequence of intron 8 has a cytosine at
position 158. As non-limiting examples, a JW1 variant allele can
have a cytosine (c) to adenine (a), cytosine (c) to guanine (g), or
cytosine (c) to thymine (t) substitution at nucleotide 158 of
intron 8. In one embodiment, the JW1 variant allele is a change
from a cytosine (c) to a thymine (t) at nucleotide 158 of NOD2
intron 8.
[0390] The term "JW15 variant allele" includes a genetic variation
in the 3' untranslated region of NOD2 at nucleotide position
118,790 of the AC007728 sequence. The genetic variation at
nucleotide 118,790 can be, but is not limited to, a single
nucleotide substitution, multiple nucleotide substitutions, or a
deletion or insertion of one or more nucleotides. The wild-type
sequence has an adenine (a) at position 118,790. As non-limiting
examples, a JW15 variant allele can have an adenine (a) to cytosine
(c), adenine (a) to guanine (g), or adenine (a) to thymine (t)
substitution at nucleotide 118,790. In one embodiment, the JW15
variant allele is a change from an adenine (a) to a cytosine (c) at
nucleotide 118,790.
[0391] As used herein, the term "JW16 variant allele" includes a
genetic variation in the 3' untranslated region of NOD2 at
nucleotide position 118,031 of the AC007728 sequence. The genetic
variation at nucleotide 118,031 can be, but is not limited to, a
single nucleotide substitution, multiple nucleotide substitutions,
or a deletion or insertion of one or more nucleotides. The
wild-type sequence has a guanine (g) at position 118,031. As
non-limiting examples, a JW16 variant allele can have a guanine (g)
to cytosine (c), guanine (g) to adenine (a), or guanine (g) to
thymine (t) substitution at nucleotide 118,031. In one embodiment,
the JW16 variant allele is a change from a guanine (g) to an
adenine (a) at nucleotide 118,031.
[0392] The term "JW17 variant allele" includes a genetic variation
in the 5' untranslated region of NOD2 at nucleotide position
154,688 of the AC007728 sequence. The genetic variation at
nucleotide 154,688 can be, but is not limited to, a single
nucleotide substitution, multiple nucleotide substitutions, or a
deletion or insertion of one or more nucleotides. The wild-type
sequence has a cytosine (c) at position 154,688. As non-limiting
examples, a JW17 variant allele can have a cytosine (c) to guanine
(g), cytosine (c) to adenine (a), or cytosine (c) to thymine (t)
substitution at nucleotide 154,688. In one embodiment, the JW17
variant allele is a change from a cytosine (c) to a thymine (t) at
nucleotide 154,688.
[0393] As used herein, the term "JW18 variant allele" includes a
genetic variation in the 5' untranslated region of NOD2 at
nucleotide position 154,471 of the AC007728 sequence. The genetic
variation at nucleotide 154,471 can be, but is not limited to, a
single nucleotide substitution, multiple nucleotide substitutions,
or a deletion or insertion of one or more nucleotides. The
wild-type sequence has a cytosine (c) at position 154,471. As
non-limiting examples, a JW18 variant allele can have a cytosine
(c) to guanine (g), cytosine (c) to adenine (a), or cytosine (c) to
thymine (t) substitution at nucleotide 154,471. In one embodiment,
the JW18 variant allele is a change from a cytosine (c) to a
thymine (t) at nucleotide 154,471.
[0394] It is understood that the methods of the invention can be
practiced with these or other NOD2 variant alleles located in a
coding region or non-coding region (e.g., intron or promoter
region) of the NOD2 locus. It is further understood that the
methods of the invention can involve determining the presence of
one, two, three, four, or more NOD2 variants, including, but not
limited to, the SNP 8, SNP 12, and SNP 13 alleles, and other coding
as well as non-coding region variants.
II. STATISTICAL ANALYSIS
[0395] In some aspects, the present invention provides methods for
selecting anti-TNF drug therapy, optimizing anti-TNF drug therapy,
reducing toxicity associated with anti-TNF drug therapy, and/or
monitoring the efficacy of anti-TNF drug treatment by applying a
statistical algorithm to one or more (e.g., a combination of two,
three, four, five, six, seven, or more) biochemical markers,
serological markers, and/or genetic markers to generate a disease
activity profile (DAP). In particular embodiments, quantile
analysis is applied to the presence, level, and/or genotype of one
or more markers to guide treatment decisions for patients receiving
anti-TNF drug therapy. In other embodiments, one or a combination
of two of more learning statistical classifier systems are applied
to the presence, level, and/or genotype of one or more markers to
guide treatment decisions for patients receiving anti-TNF drug
therapy. The statistical analyses of the methods of the present
invention advantageously provide improved sensitivity, specificity,
negative predictive value, positive predictive value, and/or
overall accuracy for selecting an initial anti-TNF drug therapy and
for determining when or how to adjust or modify (e.g., increase or
decrease) the subsequent dose of an anti-TNF drug, to combine an
anti-TNF drug (e.g., at an increased, decreased, or same dose) with
one or more immunosuppressive agents such as methotrexate (MTX) or
azathioprine (AZA), and/or to change the current course of therapy
(e.g., switch to a different anti-TNF drug).
[0396] The term "statistical analysis" or "statistical algorithm"
or "statistical process" includes any of a variety of statistical
methods and models used to determine relationships between
variables. In the present invention, the variables are the
presence, level, or genotype of at least one marker of interest.
Any number of markers can be analyzed using a statistical analysis
described herein. For example, the presence or level of 1, 2, 3, 4,
5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 30,
35, 40, 45, 50, 55, 60, or more markers can be included in a
statistical analysis. In one embodiment, logistic regression is
used. In another embodiment, linear regression is used. In yet
another embodiment, ordinary least squares regression or
unconditional logistic regression is used. In certain preferred
embodiments, the statistical analyses of the present invention
comprise a quantile measurement of one or more markers, e.g.,
within a given population, as a variable. Quantiles are a set of
"cut points" that divide a sample of data into groups containing
(as far as possible) equal numbers of observations. For example,
quartiles are values that divide a sample of data into four groups
containing (as far as possible) equal numbers of observations. The
lower quartile is the data value a quarter way up through the
ordered data set; the upper quartile is the data value a quarter
way down through the ordered data set. Quintiles are values that
divide a sample of data into five groups containing (as far as
possible) equal numbers of observations. The present invention can
also include the use of percentile ranges of marker levels (e.g.,
tertiles, quartile, quintiles, etc.), or their cumulative indices
(e.g., quartile sums of marker levels to obtain quartile sum scores
(QSS), etc.) as variables in the statistical analyses (just as with
continuous variables).
[0397] In certain embodiments, the present invention involves
detecting or determining the presence, level (e.g., magnitude),
and/or genotype of one or more markers of interest using quartile
analysis. In this type of statistical analysis, the level of a
marker of interest is defined as being in the first quartile
(<25%), second quartile (25-50%), third quartile (51%-<75%),
or fourth quartile (75-100%) in relation to a reference database of
samples. These quartiles may be assigned a quartile score of 1, 2,
3, and 4, respectively. In certain instances, a marker that is not
detected in a sample is assigned a quartile score of 0 or 1, while
a marker that is detected (e.g., present) in a sample (e.g., sample
is positive for the marker) is assigned a quartile score of 4. In
some embodiments, quartile 1 represents samples with the lowest
marker levels, while quartile 4 represent samples with the highest
marker levels. In other embodiments, quartile 1 represents samples
with a particular marker genotype (e.g., wild-type allele), while
quartile 4 represent samples with another particular marker
genotype (e.g., allelic variant). The reference database of samples
can include a large spectrum of patients with a TNF.alpha.-mediated
disease or disorder such as, e.g., IBD. From such a database,
quartile cut-offs can be established. A non-limiting example of
quartile analysis suitable for use in the present invention is
described in, e.g., Mow et al., Gastroenterology, 126:414-24
(2004).
[0398] In some embodiments, the statistical analyses of the present
invention comprise one or more learning statistical classifier
systems. As used herein, the term "learning statistical classifier
system" includes a machine learning algorithmic technique capable
of adapting to complex data sets (e.g., panel of markers of
interest) and making decisions based upon such data sets. In some
embodiments, a single learning statistical classifier system such
as a decision/classification tree (e.g., random forest (RF) or
classification and regression tree (C&RT)) is used. In other
embodiments, a combination of 2, 3, 4, 5, 6, 7, 8, 9, 10, or more
learning statistical classifier systems are used, preferably in
tandem. Examples of learning statistical classifier systems
include, but are not limited to, those using inductive learning
(e.g., decision/classification trees such as random forests,
classification and regression trees (C&RT), boosted trees,
etc.), Probably Approximately Correct (PAC) learning, connectionist
learning (e.g., neural networks (NN), artificial neural networks
(ANN), neuro fuzzy networks (NFN), network structures, the Cox
Proportional-Hazards Model (CPHM), perceptrons such as multi-layer
perceptrons, multi-layer feed-forward networks, applications of
neural networks, Bayesian learning in belief networks, etc.),
reinforcement learning (e.g., passive learning in a known
environment such as naive learning, adaptive dynamic learning, and
temporal difference learning, passive learning in an unknown
environment, active learning in an unknown environment, learning
action-value functions, applications of reinforcement learning,
etc.), and genetic algorithms and evolutionary programming. Other
learning statistical classifier systems include support vector
machines (e.g., Kernel methods), multivariate adaptive regression
splines (MARS), Levenberg-Marquardt algorithms, Gauss-Newton
algorithms, mixtures of Gaussians, gradient descent algorithms, and
learning vector quantization (LVQ).
[0399] Random forests are learning statistical classifier systems
that are constructed using an algorithm developed by Leo Breiman
and Adele Cutler. Random forests use a large number of individual
decision trees and decide the class by choosing the mode (i.e.,
most frequently occurring) of the classes as determined by the
individual trees. Random forest analysis can be performed, e.g.,
using the RandomForests software available from Salford Systems
(San Diego, Calif.). See, e.g., Breiman, Machine Learning, 45:5-32
(2001); and
http://stat-www.berkeley.edu/users/breiman/RandomForests/cc_home.htm,
for a description of random forests.
[0400] Classification and regression trees represent a computer
intensive alternative to fitting classical regression models and
are typically used to determine the best possible model for a
categorical or continuous response of interest based upon one or
more predictors. Classification and regression tree analysis can be
performed, e.g., using the C&RT software available from Salford
Systems or the Statistica data analysis software available from
StatSoft, Inc. (Tulsa, Okla.). A description of classification and
regression trees is found, e.g., in Breiman et al. "Classification
and Regression Trees," Chapman and Hall, New York (1984); and
Steinberg et al., "CART: Tree-Structured Non-Parametric Data
Analysis," Salford Systems, San Diego, (1995).
[0401] Neural networks are interconnected groups of artificial
neurons that use a mathematical or computational model for
information processing based on a connectionist approach to
computation. Typically, neural networks are adaptive systems that
change their structure based on external or internal information
that flows through the network. Specific examples of neural
networks include feed-forward neural networks such as perceptrons,
single-layer perceptrons, multi-layer perceptrons, backpropagation
networks, ADALINE networks, MADALINE networks, Learnmatrix
networks, radial basis function (RBF) networks, and self-organizing
maps or Kohonen self-organizing networks; recurrent neural networks
such as simple recurrent networks and Hopfield networks; stochastic
neural networks such as Boltzmann machines; modular neural networks
such as committee of machines and associative neural networks; and
other types of networks such as instantaneously trained neural
networks, spiking neural networks, dynamic neural networks, and
cascading neural networks. Neural network analysis can be
performed, e.g., using the Statistica data analysis software
available from StatSoft, Inc. See, e.g., Freeman et al., In "Neural
Networks: Algorithms, Applications and Programming Techniques,"
Addison-Wesley Publishing Company (1991); Zadeh, Information and
Control, 8:338-353 (1965); Zadeh, "IEEE Trans. on Systems, Man and
Cybernetics," 3:28-44 (1973); Gersho et al., In "Vector
Quantization and Signal Compression," Kluywer Academic Publishers,
Boston, Dordrecht, London (1992); and Hassoun, "Fundamentals of
Artificial Neural Networks," MIT Press, Cambridge, Massachusetts,
London (1995), for a description of neural networks.
[0402] Support vector machines are a set of related supervised
learning techniques used for classification and regression and are
described, e.g., in Cristianini et al., "An Introduction to Support
Vector Machines and Other Kernel-Based Learning Methods," Cambridge
University Press (2000). Support vector machine analysis can be
performed, e.g., using the SVM.sup.light software developed by
Thorsten Joachims (Cornell University) or using the LIBSVM software
developed by Chih-Chung Chang and Chih-Jen Lin (National Taiwan
[0403] University).
[0404] The various statistical methods and models described herein
can be trained and tested using a cohort of samples (e.g.,
serological and/or genomic samples) from healthy individuals and
patients with a TNF.alpha.-mediated disease or disorder such as,
e.g., IBD (e.g., CD and/or UC). For example, samples from patients
diagnosed by a physician, preferably by a gastroenterologist, as
having IBD or a clinical subtype thereof using a biopsy,
colonoscopy, or an immunoassay as described in, e.g., U.S. Pat. No.
6,218,129, are suitable for use in training and testing the
statistical methods and models of the present invention. Samples
from patients diagnosed with IBD can also be stratified into
Crohn's disease or ulcerative colitis using an immunoassay as
described in, e.g., U.S. Pat. Nos. 5,750,355 and 5,830,675. Samples
from healthy individuals can include those that were not identified
as IBD samples. One skilled in the art will know of additional
techniques and diagnostic criteria for obtaining a cohort of
patient samples that can be used in training and testing the
statistical methods and models of the present invention.
[0405] As used herein, the term "sensitivity" includes the
probability that a method of the present invention for selecting
anti-TNF drug therapy, optimizing anti-TNF drug therapy, reducing
toxicity associated with anti-TNF drug therapy, and/or monitoring
the efficacy of anti-TNF drug treatment gives a positive result
when the sample is positive, e.g., having the predicted therapeutic
response to anti-TNF drug therapy or toxicity associated with
anti-TNF drug therapy. Sensitivity is calculated as the number of
true positive results divided by the sum of the true positives and
false negatives. Sensitivity essentially is a measure of how well
the present invention correctly identifies those who have the
predicted therapeutic response to anti-TNF drug therapy or toxicity
associated with anti-TNF drug therapy from those who do not have
the predicted therapeutic response or toxicity. The statistical
methods and models can be selected such that the sensitivity is at
least about 60%, and can be, e.g., at least about 65%, 70%, 75%,
76%, 77%, 78%, 79%, 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%,
89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99%.
[0406] The term "specificity" includes the probability that a
method of the present invention for selecting anti-TNF drug
therapy, optimizing anti-TNF drug therapy, reducing toxicity
associated with anti-TNF drug therapy, and/or monitoring the
efficacy of anti-TNF drug treatment gives a negative result when
the sample is not positive, e.g., not having the predicted
therapeutic response to anti-TNF drug therapy or toxicity
associated with anti-TNF drug therapy. Specificity is calculated as
the number of true negative results divided by the sum of the true
negatives and false positives. Specificity essentially is a measure
of how well the present invention excludes those who do not have
the predicted therapeutic response to anti-TNF drug therapy or
toxicity associated with anti-TNF drug therapy from those who do
have the predicted therapeutic response or toxicity. The
statistical methods and models can be selected such that the
specificity is at least about 60%, and can be, e.g., at least about
65%, 70%, 75%, 76%, 77%, 78%, 79%, 80%, 81%, 82%, 83%, 84%, 85%,
86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or
99%.
[0407] The term "negative predictive value" or "NPV" includes the
probability that an individual identified as not having the
predicted therapeutic response to anti-TNF drug therapy or toxicity
associated with anti-TNF drug therapy actually does not have the
predicted therapeutic response or toxicity. Negative predictive
value can be calculated as the number of true negatives divided by
the sum of the true negatives and false negatives. Negative
predictive value is determined by the characteristics of the
methods of the present invention as well as the prevalence of the
disease in the population analyzed. The statistical methods and
models can be selected such that the negative predictive value in a
population having a disease prevalence is in the range of about 70%
to about 99% and can be, for example, at least about 70%, 75%, 76%,
77%, 78%, 79%, 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%,
90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99%.
[0408] The term "positive predictive value" or "PPV" includes the
probability that an individual identified as having the predicted
therapeutic response to anti-TNF drug therapy or toxicity
associated with anti-TNF drug therapy actually has the predicted
therapeutic response or toxicity. Positive predictive value can be
calculated as the number of true positives divided by the sum of
the true positives and false positives. Positive predictive value
is determined by the characteristics of the methods of the present
invention as well as the prevalence of the disease in the
population analyzed. The statistical methods and models can be
selected such that the positive predictive value in a population
having a disease prevalence is in the range of about 70% to about
99% and can be, for example, at least about 70%, 75%, 76%, 77%,
78%, 79%, 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%,
91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99%.
[0409] Predictive values, including negative and positive
predictive values, are influenced by the prevalence of the disease
in the population analyzed. In the present invention, the
statistical methods and models can be selected to produce a desired
clinical parameter for a clinical population with a particular
prevalence for a TNF.alpha.-mediated disease or disorder such as,
e.g., IBD. As a non-limiting example, statistical methods and
models can be selected for an IBD prevalence of up to about 1%, 2%,
3%, 4%, 5%, 6%, 7%, 8%, 9%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%,
50%, 55%, 60%, 65%, or 70%, which can be seen, e.g., in a
clinician's office such as a gastroenterologist's office or a
general practitioner's office.
[0410] As used herein, the term "overall agreement" or "overall
accuracy" includes the accuracy with which a method of the present
invention selects anti-TNF drug therapy, optimizes anti-TNF drug
therapy, reduces toxicity associated with anti-TNF drug therapy,
and/or monitors the efficacy of anti-TNF drug treatment. Overall
accuracy is calculated as the sum of the true positives and true
negatives divided by the total number of sample results and is
affected by the prevalence of the disease in the population
analyzed. For example, the statistical methods and models can be
selected such that the overall accuracy in a patient population
having a disease prevalence is at least about 40%, and can be,
e.g., at least about 40%, 41%, 42%, 43%, 44%, 45%, 46%, 47%, 48%,
49%, 50%, 51%, 52%, 53%, 54%, 55%, 56%, 57%, 58%, 59%, 60%, 61%,
62%, 63%, 64%, 65%, 66%, 67%, 68%, 69%, 70%, 71%, 72%, 73%, 74%,
75%, 76%, 77%, 78%, 79%, 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%,
88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99%.
III. EXAMPLES
[0411] The present invention will be described in greater detail by
way of specific examples. The following examples are offered for
illustrative purposes, and are not intended to limit the invention
in any manner. Those of skill in the art will readily recognize a
variety of noncritical parameters which can be changed or modified
to yield essentially the same results.
[0412] The Examples set forth in U.S. Provisional Application No.
61/444,097, filed Feb. 17, 2011, and PCT Application No.
PCT/US2010/054125, filed Oct. 26, 2010, are hereby incorporated by
reference in their entirety for all purposes.
Example 1
Disease Activity Profiling for Identifying Responders and
Non-Responders to Anti-TNF.alpha. Biologics
[0413] This example describes methods for personalized therapeutic
management of a TNF.alpha.-mediated disease in order to optimize
therapy or monitor therapeutic efficacy in a subject using the
disease activity profiling of the present invention to identify
subjects as responders or non-responders to anti-TNF drug
therapy.
[0414] FIG. 1 illustrates an exemplary IBD wound response profile
in which wound progression is divided into inflammatory,
proliferative, and remodeling phases. As non-limiting examples,
inflammatory response phase markers tested include: anti-TNF drugs
such as Remicade (infliximab); anti-drug antibodies (ADA) such as
HACA; inflammatory markers such as GM-CSF, IFN-.gamma., IL-1.beta.,
IL-2, IL-6, IL-8, TNF-.alpha., and sTNF RII; and anti-inflammatory
markers such as IL-12p70 and IL-10. Non-limiting examples of
proliferation response phase markers tested include tissue
repair/remodeling factors (also referred to as mucosal healing
markers) such as AREG, EREG, HB-EGF, HGF, NRG1, NRG2, NRG3, NRG4,
BTC, EGF, IGF, TGF-.alpha., VEGF-A, VEGF-B, VEGF-C, VEGF-D, FGF1,
FGF2, FGF7, FGF9, and TWEAK.
[0415] A COMMIT (Combination Of Maintenance Methotrexate-Infliximab
Trial) study was performed to evaluate the safety and efficacy of
Remicade (infliximab) in combination with methotrexate for the
long-term treatment of Crohn's disease (CD). Treatment success was
defined by the proportion of subjects in clinical remission (i.e.,
complete discontinuation of prednisone therapy and a Crohn's
Disease Activity Index (CDAI) score of <150) at week 14, and
maintenance of clinical remission between study weeks 14 and 50. In
particular, clinical assessment with CDAI was performed at week 0,
46, 50, and 66. Subjects with CDAI>150 were identified as
non-responders. Additional information on the COMMIT study is
provided at http://www.clinicaltrials.gov/ct2/show/NCT00132899, the
disclosure of which is incorporated by reference in its entirety
for all purposes.
[0416] Disease activity profiling was performed on a number of
subjects in the COMMIT study. In particular, the following array of
markers were measured at various time points during treatment with
Remicade (infliximab) only or a combination of Remicade
(infliximab) with methotrexate: (1) Remicade (infliximab) and HACA;
(2) inflammatory markers GM-CSF, IFN-.gamma., IL-1.beta., IL-2,
IL-6, IL-8, TNF-.alpha., and sTNF RII; (3) anti-inflammatory
markers IL-12p70 and IL-10; and (4) tissue repair markers EGF,
bFGF, PIGF, sFltl, and VEGF. The disease activity profile (DAP) for
7 of these subjects, which provides a comparison between responder
and non-responder profiles, is illustrated herein. These patient
examples show that markers for inflammation and tissue repair
correlated with infliximab and HACA levels in select active CD
patients, certain markers may predict the disease activity profile,
and disease activity profiling will further guide patient therapy
and identify mucosal healing markers. In addition, these patient
examples show that whenever anti-inflammatory cytokines such as
IL-12p70 and IL-10 are elevated, the patient responds, indicating
that they may be markers of mucosal healing, and that tissue repair
markers (TRM) go up in non-responders.
TABLE-US-00005 Table of Personalized Disease Activity Profiling:
Levels of IFX, HACA, Inflammatory Markers, Anti-Inflammatory
Markers, and Mucosal Healing Markers Anti- Mucosal Patient
Treatment Clinical Inflammatory inflammatory Healing ID No. Regimen
CDAI Definition IFX HACA Markers Markers Markers 12209 IFX + MTX t
= 0, CDAI was Non- Low at HACA+. HIGH LOW MEDIUM 202. responder
trough LOW t = wk 26, CDAI (wk 14) was 183 t = wk 66, CDAI = 152.
11010 IFX t = 0, CDAI was Responder High at HACA-. LOW HIGH HIGH
262. trough ND t = wk 46, CDAI (wk 14) was 85. 10118 IFX t = 0,
CDAI was Responder High at HACA-. MEDIUM HIGH HIGH 251. trough ND t
= wk 46, CDAI (wk 14) was 109. 11602 IFX + MTX t = 0, CDAI was
Responder High at HACA-. LOW HIGH HIGH 217. trough ND t = wk 46,
CDAI (wk 14) was 68. 11505 IFX t = 0, CDAI was Non- Very Low
HACA++. MEDIUM LOW HIGH 272. responder at trough HIGH t = wk 46,
CDAI (wk 14) was 145. t = wk 66, CDAI = 195. 11601 IFX + MTX t = 0,
CDAI was Responder High at HACA+. HIGH HIGH MEDIUM 207. trough LOW
t = wk 46, CDAI (wk 14) was 0. IFX = infliximab. MTX =
methotrexate. ND = no detectable level of HACA.
Patient 12209: Infliximab+Methotrexate (MTX) Treated.
[0417] CDAI at time 0 was 202. At week 46, CDAI was 183 ("Delta 19"
or 202-19=183). At week 66, CDAI was 152 ("Delta 50" or
202-50=152). Clinically defined as non-responder. Disease activity
profile (DAP) accurately identified this patient. In particular,
DAP showed that this patient had low infliximab (IFX) levels at
trough ("T"; Week 14), the presence of a detectable concentration
level of HACA ("HACA +"), high inflammatory marker levels, low
anti-inflammatory marker levels, and medium tissue repair marker
(TRM) levels. Suggested alternative treatment options may include,
for example, increasing the dose of IFX, switching to therapy with
adalimumab (HUMIRA.TM.), treating with a different
immunosuppressive drug such as azathioprine (AZA), and/or switching
to therapy with a drug that targets a different mechanism (e.g., an
anti-INF.gamma. antibody such as fontolizumab).
Patient 11010: Infliximab Treated.
[0418] CDAI at week 0 was 262. At week 46, CDAI was 85 ("Delta 177"
or 262-177=85). Clinical responder. Disease activity profile (DAP)
accurately identified this patient. In particular, DAP showed that
this patient had high infliximab (IFX) levels at trough ("T"; Week
14), no detectable level of HACA ("HACA --"), low inflammatory
marker levels, high anti-inflammatory marker levels, and high
tissue repair marker (TRM) levels. For example, anti-inflammatory
cytokines IL-12p70 and IL-10 were high. As shown with the patients
in this example, whenever anti-inflammatory cytokines were high,
the patient responded most probably with mucosal healing. In
addition, bFGF concentration levels were low at all time points,
although other TRM levels were high, indicating that tissue growth
was muted, such that tissue repair had already occurred.
Patient 10118: Infliximab Treated.
[0419] CDAI at week 0 was 251. At week 46, CDAI was 109 ("Delta
142" or 251-142=109). Clinical responder. Disease activity profile
(DAP) accurately identified this patient. In particular, DAP showed
that this patient had high infliximab (IFX) levels at trough ("T";
Week 14), no detectable level of HACA ("HACA --"), medium
inflammatory marker levels, high anti-inflammatory marker levels,
and high tissue repair marker (TRM) levels. For example,
anti-inflammatory cytokines IL-12p70 and IL-10 were high. Again, as
shown with the patients in this example, whenever anti-inflammatory
cytokines were high, the patient responded most probably with
mucosal healing. In addition, bFGF concentration levels were low at
all time points and remained flat over the course of therapy,
although other TRM levels were higher, indicating that tissue
growth was muted, such that tissue repair had already occurred.
Patient 11602: Infliximab+Methotrexate (MTX) Treated.
[0420] CDAI at week 0 was 217. At week 46, CDAI was 68 ("Delta 149"
or 217-149=68). Clinical responder. Disease activity profile (DAP)
accurately identified this patient. In particular, DAP showed that
this patient had high infliximab (IFX) levels at trough ("T"; Week
14), no detectable level of HACA ("HACA --"), low inflammatory
marker levels, high anti-inflammatory marker levels, and high
tissue repair marker (TRM) levels. For example, anti-inflammatory
cytokines IL-12p70 and IL-10 were high. Again, as shown with the
patients in this example, whenever anti-inflammatory cytokines were
high, the patient responded most probably with mucosal healing. In
addition, bFGF concentration levels were lower at all time points
compared to the other TRM levels, indicating that tissue growth was
muted, such that tissue repair had already occurred.
Patient 11505: Infliximab Treated.
[0421] CDAI at time 0 was 272. At week 46, CDAI was 145 ("Delta
127" or 272-127=145). At week 66, CDAI was 195. Clinically defined
as non-responder. Disease activity profile (DAP) accurately
identified this patient. In particular, DAP showed that this
patient had very low infliximab (IFX) levels at trough ("T"; Week
14), a high concentration level of HACA ("HACA ++"), medium
inflammatory marker levels, low anti-inflammatory marker levels,
and high tissue repair marker (TRM) levels. In non-responders, the
levels of TRM such as bFGF go up, while in responders they either
go down or do not change. Suggested alternative treatment options
may include, for example, increasing the dose of IFX, switching to
therapy with adalimumab (HUMIRA.TM.), treating with an
immunosuppressive drug such as MTX or azathioprine (AZA), and/or
switching to therapy with a drug that targets a different mechanism
(e.g., an anti-INF.gamma. antibody such as fontolizumab).
Patient 11601: Infliximab+Methotrexate (MTX) Treated.
[0422] CDAI at week 0 was 207. At week 46, CDAI was 0 ("Delta 207"
or 207-207=0). The patient was clinically defined as responder.
Disease activity profile (DAP) accurately identified this patient.
In particular, DAP showed that this patient had high infliximab
(IFX) levels at trough ("T"; Week 14), low HACA levels ("HACA +"),
high inflammatory marker levels, high anti-inflammatory marker
levels, and medium tissue repair marker (TRM) levels. For example,
anti-inflammatory cytokines IL-12p70 and IL-10 were high. Again, as
shown with the patients in this example, whenever anti-inflammatory
cytokines were high, the patient responded most probably with
mucosal healing, clearly indicating that anti-inflammatory markers
are very important. The presence of high inflammation may be due to
complication.
Patient 10113: Infliximab Treated.
[0423] CDAI at time 0 was 150. At week 46, CDAI was 96 ("Delta 54"
or 150-54=96). At visit 10 ("V10"), CDAI was 154, and at visit 11
("V11"), CDAI was 169. As such, CDAI started at 150 and stayed
around 150. The patient was clinically defined as non-responder.
Disease activity profile (DAP) accurately identified this patient.
In particular, DAP showed that this patient had low infliximab
(IFX) levels at trough ("T"; Week 14), a detectable concentration
level of HACA ("HACA +"), medium inflammatory marker levels, low
anti-inflammatory marker levels, and medium tissue repair marker
(TRM) levels. Again, TRM levels go up in non-responders, while in
responders they either go down or do not change. Suggested
alternative treatment options may include, for example, increasing
the dose of IFX, switching to therapy with adalimumab (HUMIRA.TM.),
treating with an immunosuppressive drug such as MTX or
azathioprine, and/or switching to therapy with a drug that targets
a different mechanism (e.g., an anti-INF.gamma. antibody such as
fontolizumab).
Example 2
Disease Activity Profiling Modeling
[0424] An exemplary 3-dimensional graph rendering of the disease
activity profile (DAP) of the present invention includes each of
the different markers present in the array of markers on the
x-axis, normalized marker levels on the y-axis, and time on the
z-axis (e.g., time points wherein samples are taken and marker
levels measured). An exemplary topographic map of the DAP of the
present invention (also referred to herein as a personalized
disease profile) includes each of the different markers present in
the array of markers the y-axis, time on the x-axis (e.g., time
points wherein samples are taken and marker levels measured), and
relative marker levels in grayscale.
[0425] The 3D models described herein represent a novel paradigm
for treatment because they are individualized and titratable such
that dose adjustments are made in a personalized manner. For
example, marker panels including markers such as inflammatory,
proliferative, and remodeling markers enable a determination in
real-time of the best course of treatment for a patient on therapy
such as anti-TNF drug therapy, e.g., for treating CD or RA. As a
result, both the time course and the concentration levels of
markers in the panel or array of markers are important for therapy
adjustment and monitoring to personalize and individualize therapy
and determine optimal doses or dose adjustments. In certain
instances, the change in one or more marker levels over time is an
important consideration for therapy adjustment and monitoring. In
particular embodiments, the desired therapeutic zone for the set or
a subset of the markers in the array or panel is within a defined
range in the 3D graph or topographic map.
Example 3
Infliximab Non-Detection
[0426] This example represents a model for "time-to-event." In
other words, this example uses the Cox Proportional-Hazards Model
(CPHM) to model the time it takes for "an event" to occur and the
risk of such an event happening. The model is a regression analysis
with "time-to-event" on the Y axis, which is a response variable,
and "predictor variables" on the X axis. In this example, the
non-detection of infliximab (i.e., the concentration of infliximab
falling below a detection threshold) is the event, with the
potential predictors of such an event being biomarkers: e.g., CRP,
IL-2, VEGF, and the like and or clinical information such as age,
MTX treatment, gender, and the like.
[0427] In this example, the "Hazard" is the risk of infliximab not
being detected (e.g., non-detection) by an analytical assay such as
a mobility shift assay. For example, FIG. 11 shows infliximab
concentration levels for various patients during their course of
treatment. An event occurs in this example when the concentration
of infliximab falls below a predetermined detection threshold. In
certain instances, the CPHM is being used to predict the risk of
the event occurring (infliximab non-detection). The example also
identifies biomarkers indicative of such a risk occurring.
[0428] Using the CPHM, time is modeled until infliximab is not
detectable by a mobility shift assay. In the model, the
predetermined threshold is 0.67 .mu.g/mL, which is the lower bound
of the reference range. If the infliximab concentration level is
less than the threshold at time "t," then the event has occurred at
time "t." In FIG. 12, patients were ranked by their time to the
event. The event occurred for various patients at different points
during treatment and is denoted with a bullet point.
[0429] In the initial model, there were various markers and
clinical information used to predict the hazard or the risk of
infliximab non-detection by the mobility shift assay. These markers
included the following markers in the Table:
TABLE-US-00006 EGF IL-1.beta. VCAM-1 bFGF IL-2 AGE PIGF IL-6 Months
since diagnosis sFlt1 IL-8 Disease @ colon VEGF TNF-.alpha. Disease
@ small intestine GM-CSF sTNFRII MTX treatment IFN-.gamma. CRP
Success IL-10 SAA IL-12p70 ICAM-1
[0430] From the initial marker list, the following list was derived
as being the preferred markers indicative of the event:
TABLE-US-00007 GM-CSF sRNFRII Disease @ small intestine IL-2 SAA
Success IL-6 ICAM-1 TNF-.alpha. Months since Dx
[0431] The following Table lists the significant predictors of
infliximab non-detection risk or the hazard:
TABLE-US-00008 Predictor coef exp (coef) se (coef) p GM-CSF
-1.92E-01 0.826 9.48E-02 4.34E-02 IL-2 1.42E-01 1.153 1.92E-02
1.63E-13 TNF-.alpha. 2.33E-02 1.024 7.57E-03 2.11E-03 sTNFRII
3.57E-01 1.429 5.76E-02 5.67E-10 SAA 6.13E-06 1.000 1.90E-06
1.25E-03 Months since -3.20E-03 0.997 1.45E-03 2.68E-02 Dx Disease
@ 1.10E+00 2.995 4.46E-01 1.39E-02 small intestine Success 8.84E-01
2.421 3.13E-01 4.72E-03
[0432] The results in the above Table indicate the following are
predictors of the hazard i.e., risk of the non-detection of
infliximab:
[0433] GM-CSF: holding all other variables constant, an extra
ng/.mu.l of GM-CSF reduces the weekly hazard of infliximab
non-detection by a factor of 0.826, or 17.4%.
[0434] IL-2: An additional 1 ng/.mu.l of IL-2 increases the hazard
by a factor of 1.153, or 15.3%.
[0435] TNF-.alpha.: A 1 ng/.mu.l of TNF-.alpha. increases the
hazard by a factor of 1.024/2.4%.
[0436] sTNFRII: A 1 ng/.mu.l of sTNFRII increases the hazard by a
factor of 1.429/42.9%.
[0437] SAA: A 1 ng/.mu.l of SAA increases the hazard by a factor of
1.000006/0.0006%, which is very small, but still a detectable
effect (small SE).
[0438] Months since diagnosis: Each additional month since
diagnosis decreases the hazard by a factor of 0.997, or 0.3%.
[0439] Disease site at the small intestine (categorical variable):
If the disease is located at the small intestine, the hazard is
increased by a factor of 2.995, or nearly 200%.
[0440] Success (categorical variable): Also a predictive of hazard;
in non-successful patients the hazard is increased by a factor of
2.421 or 142%.
[0441] In summary, the following markers appear to be good
predictors of infliximab "clearance"/or non-detection: 1) GM-CSF;
2) IL-2; 3) TNF-.alpha.; 4) sTNFRII; and 5) the disease being
situated in the small intestine.
[0442] As such, in one embodiment, the present invention
provides:
[0443] A method for predicting the likelihood the concentration of
an anti-TNF therapeutic or antibody during the course of treatment
will fall below a threshold value, the method comprising:
[0444] measuring a panel of markers selected from the group
consisting of 1) GM-CSF; 2) IL-2; 3) TNF-.alpha.; 4) sTNFRII; and
5) the disease being situated in the small intestine; and
[0445] predicting the likelihood the concentration of an anti-TNF
therapeutic or antibody will fall below the threshold based upon
the concentration of the markers.
Example 4
Detection of Antidrug Antibody to Infliximab ("ATI" or "HACA")
[0446] This example uses the Cox Proportional-Hazards Model (CPHM)
to model the time that it takes for an event to occur. This is a
similar analysis to Example 3 above, but with the appearance of the
anti-drug antibody also known as ATI or HACA as the event and risk
of ATI formation (detection) as the hazard. FIG. 13 shows the
concentration of ATI (HACA) in various patients during the course
of treatment. In FIG. 14, patients were ranked by their time to the
event. The event occurred for various patients at different points
during treatment and is denoted with a bullet point. The risk of
ATI detection is the hazard. Significant predictors of the hazard
include:
TABLE-US-00009 Predictor coef exp (coef) se (coef) p EGF -2.33E-03
0.998 1.18E-03 7.82E-03 VEGF 1.37E-03 1.001 4.10E-04 8.64E-04
GM-CSF -2.72E-01 0.762 1.06E-01 1.06E-02 IL-2 6.15E-01 1.850
2.81E-01 2.83E-02 IL-8 3.58E-04 1.000 1.22E-04 3.25E-03 TNF-.alpha.
2.37E-02 1.024 8.76E-03 6.81E-03 CRP 3.09E-05 1.000 1.04E-05
3.00E-03 VCAM 1.28E-03 1.001 2.01E-04 1.87E-10
[0447] The data in the above table indicates that EGF, VEGF, IL-8,
CRP and VCAM-1 all have very small, but significant effects on the
hazard.
[0448] GM-CSF: Holding all other variables constant, an extra
ng/.mu.l of GM-CSF reduces the weekly hazard of ATI detection by a
factor of 0.762, or 27.4%.
[0449] IL-2: A 1 ng/.mu.l increase of IL-2 increases the hazard by
a factor of 1.85, or 85%.
[0450] TNF-.alpha.: A 1 ng/.mu.l increase of TNF-.alpha. increases
hazard by a factor of 1.024, or 2.4%.
[0451] In summary, the Predictors of ATI detection hazard are
GM-CSF, IL-2 and TNF-.alpha..
[0452] As such, in one embodiment, the present invention provides a
method for predicting the likelihood that anti-drug antibodies will
occur in an individual on anti-TNF therapy or antibodies, said
method comprising:
[0453] measuring a panel of markers selected from the group
consisting of EGF, VEGF, IL-8, CRP and VCAM-1; and
[0454] predicting the likelihood that anti-drug antibodies will
occur in an individual on anti-TNF therapy based on the
concentration of marker levels.
Example 5
Disease Activity Profiling for Crohn's Disease Prognosis Using
COMMIT Study Samples
[0455] This example illustrates methods for personalized
therapeutic management of a TNF.alpha.-mediated disease in order to
optimize therapy or monitor therapeutic efficacy in a subject using
the disease activity profiling of the present invention. This
example illustrates disease activity profiling which comprises
detecting, measuring, or determining the presence, level and or
activation of one or more specific biomarkers (e.g., drug levels,
anti-drug antibody levels, inflammatory markers, anti-inflammatory
markers, and tissue repair markers).
[0456] This example describes disease activity profiling on a
number of samples from the COMMIT study. As described in Example 1,
the COMMIT (Combination of Maintenance Methotrexate-Inflixamab
Trial) study was performed to evaluate the safety and efficacy of
Remicade (inflixamab) in combination with methotrexate (MTX) for
the long-term treatment of Crohn's Disease (CD). In particular, the
following array of markers was measured at various time points
during treatment with Remicade (infliximab; IFX) only or a
treatment of Remicade with MTX: (1) Remicade (inflixamab) and
antidrug antibodies to infliximab (ATI); (2) inflammatory markers
CRP, SAA, ICAM, VCAM; and (3) tissue repair marker VEGF. This
example shows that the markers of inflammation and tissue repair
correlated with IFX and ATI levels in select patients of
TNF-.alpha. mediated disease (e.g., Crohn's Disease and Ulcerative
Colitis). In some instances, arrays of markers may predict a
disease activity index (e.g., Crohn's Disease Activity Index).
Analysis of the COMMIT study is illustrated herein.
[0457] The relationship between the presence of ATI and serum
levels of IFX concentration was investigated. For the evaluation,
total ATI levels below the level of quantitation (BLOQ) were 3.13
U/ml, and were set to 0. IFX concentrations below the level of
detection (BLOD) were set to 0. Per the sample comparison, only
trough samples were used and a total of 219 were used in the
evaluation. 24 samples were determined to be ATI positive (ATI+).
It was determined that the median level of IFX was 0 .mu.g/ml in
ATI+ samples, while the median level of IFX was 8.373 .mu.g/ml in
ATI negative (ATI-) samples (p=3.71.times.10.sup.-9 by Mann Whitney
U test). FIG. 4A illustrates an association between the presence of
ATI and the level of IFX in patient samples. Patient samples with
no detectable level of ATI had a significantly higher IFX median
concentration, compared to ATI+ samples.
[0458] The relationship between CDAI and the presence of ATI was
evaluated. In the analysis ATI of 3.13 U/ml was set as the cut-off;
only trough samples were evaluated and ATI BLOQ was set as 0. 195
samples were ATI-, while 24 samples from a total of 4 patients were
ATI+. The results showed that the median CDAI for ATI+ samples was
121.5 while the median CDAI for ATI- samples was 82 (p=0.0132 by
Mann Whitney U test). FIG. 4B illustrates that the presence of ATI
correlates with higher CDAI. The results show that ATI+ samples
have significantly higher CDAI than ATI- samples.
[0459] The relationship between the presence of ATI and combination
therapy of IFX and immunosuppressant agent (e.g., MTX) was
investigated. ATI+ samples at any trough time point were analyzed.
The results showed that there was no significant difference in odds
of having ATI between IFX therapy alone and IFX+MTX combination
therapy. The high odds ratio (e.g., 2.851) indicates that MTX can
prevent a patient from developing an immune response to therapeutic
biologics. FIG. 4C shows that concurrent immunosuppressant therapy
(e.g., MTX) is more likely to suppress the presence of ATI.
[0460] The relationship between ATI and clinical outcome at
follow-up was also investigated. ATI+ samples at any trough time
point were analyzed. Clinical outcome as described from the
clinical data received from the study was parsed as either
"success" or "non-success". No significant difference in odds of
being ATI+ was seen regardless of treatment regimen. The low odds
ratio (e.g., 0.1855, p=0.1459) indicates that ATI+ patients tend to
have poor clinical outcomes. FIG. 5A shows that patients with ATI
are more likely to develop a poor response to treatment.
[0461] This example also illustrates an association of an exemplary
PRO Inflammatory Index and serum levels of infliximab (IFX) or the
presence of antibodies to IFX (ATI) in a patient sample. FIG. 5B
illustrates that the inflammatory marker CRP is associated with
increased levels of ATI. The data shows that the median CRP level
was 8.11 .mu.g/ml in ATI+samples and 1.73 .mu.g/ml in ATI- samples
(p=2.67.times.10.sup.-6 by Mann Whitney U Test). Other inflammatory
and tissue repair markers were evaluated. FIG. 6 illustrates that
the protein levels of an array of one or more inflammatory and
tissue repair markers correlate to the formation of antibodies to
IFX. The data shows that of a combination of five markers (e.g.,
CRP, SAA, ICAM, VCAM, VEGF and including at least one inflammatory
marker) was expressed in 23 out of 24 ATI positive samples (FIG.
7A, grey box). The inflammatory marker SAA was found to be positive
in 19 of the 24 ATI positive samples that were also clinically
described as having "high inflammation". The results also show that
VEGF and CRP are the most non-overlapping markers in the
analysis.
[0462] This example further shows an exemplary PRO Inflammatory
Index (PII). The inflammatory index score is created by logarithmic
transformation of a combination of values representing determined
expression levels of a plurality of markers (e.g.,
PII=log(CRP+SAA+ICAM+VCAM+VEGF)). FIG. 7B illustrates that an
exemplary PRO Inflammatory Index (PII) correlates with levels of
IFX (p<0.0001 and R.sup.2=-0.129) in patient samples of the
COMMIT study. The results show that ATI positive samples have a
significantly higher inflammatory index score compared to ATI
negative samples (P=6.4.times.10.sup.-8; see FIG. 7C).
[0463] As such, in one embodiment, the present invention provides a
method for monitoring an infliximab treatment regimen, said method
comprising:
[0464] a) measuring infliximab and antidrug antibodies to
infliximab (ATI);
[0465] b) measuring inflammatory markers CRP, SAA, ICAM, VCAM;
[0466] c) measuring tissue repair marker VEGF; and
[0467] d) correlating the measurements to therapeutic efficacy.
Example 6
Disease Activity Profiling for TNF-.alpha. Mediated Disease
Prognosis Using Clinical Study #1 Samples
[0468] This example describes methods for monitoring therapeutic
efficacy in a subject using the disease activity profiling of the
present invention to identify subjects as responders or
non-responders to anti-TNF drug therapy. This example illustrates
the use of disease activity profiling with a number of patient
samples from a Crohn's Disease clinical trial #1.
[0469] In particular, an array of markers was measured at various
time points during treatment with Remicade (infliximab; IFX) only
or a treatment of Remicade with MTX: Remicade (inflixamab),
antibodies to infliximab (ATI), and neutralizing antibodies to IFX.
This example shows that a disease activity profile can show the
relationship among ATI, IFX and neutralizing antibodies. Analysis
of clinical study #1 is illustrated herein.
[0470] FIG. 8A-B illustrates the correlation between Crohn's
Disease Activity Index (CDAI) score and the concentration of
infliximab in serum in a number of patients in clinical study #1.
In brief, 894 samples were analyzed. An IFX
concentration.gtoreq.0.1 .mu.g/ml at the limit of detection (LOD)
was defined to be "present". The results showed that IFX negative
(IFX-) samples also have significantly higher CDAI (p=0.0254,
calculated by Mann-Whitney U test), compared to IFX positive
samples (IFX+).
[0471] Further analysis revealed that the presence of ATI
correlates with lower IFX concentrations. It was assumed that total
ATI below the level of quantitation (BLOQ) of 3.13 U/ml was set as
0 and IFX concentration below the level of detection (BLOD) was set
at 0. It was determined that 24% of the patients (62/258) in the
study were ATI+, as defined as positive total ATI levels at one of
three time points. The analysis of 894 samples showed a correlation
between IFX concentration and ATI levels. In particular, the median
IFX was 0 .mu.g/ml for ATI+ samples and 7.95 .mu.g/ml for ATI-
samples (p<2.2.times.10.sup.-16 by Mann-Whitney U test). FIG. 9A
illustrates the association between IFX concentration and the
presence of antidrug antibodies to inflixamab in samples
analyzed.
[0472] Analysis shows that a high concentration of ATI in samples
correlates with the presence of neutralizing antibodies that target
TNF-.alpha. biologics. In some embodiments, assays can be used to
detect neutralizing antibodies. Neutralizing antibodies were
detected in patient samples with the highest concentrations of ATI.
FIG. 9B illustrates that a high concentration of ATI can lead to
the presence of neutralizing antibodies and undetectable levels of
IFX.
[0473] Longitudinal analysis of the relationship of CDAI and the
presence of ATI was evaluated in samples collected at clinic visit
#1 and #3 from 283 patients. A correlation between the presence of
ATI at visit #1 (V1) was established with CDAI at visit #3 (V3).
The median CDAI was 109 at V1 in ATI+ samples, while the median
CDAI was 78 in ATI- samples (p=0.027 by Mann Whitney U test). The
results indicate a causal relationship between ATI positivity and
CDAI. FIG. 9C illustrates that ATI+ samples determined at an early
time point were more likely to have a higher CDAI at a later time.
The results indicate that disease activity profiling at an early
time point can predict CDAI at a later time point. FIG. 9D
illustrates that in Clinical Study #1, patients had lower odds of
developing ATI if receiving a combination therapy of infliximab
(IFX) and an immunosuppressant agent (e.g., MTX and AZA). The odds
ratio was 0.320 (p=0.0009 by Fisher's Exact test). In this
analysis, ATI positivity (ATI+) was defined as total
ATI.gtoreq.3.13 U/ml.
Example 7
Disease Activity Profiling for TNF-.alpha. Mediated Disease
Prognosis Using Clinical Study #2 Samples
[0474] A. Clinical Study #2A
[0475] This example illustrates the use of a method for monitoring
therapeutic efficacy in patients receiving Remicade (inflixamab)
alone or in combination with an immunosuppressant agent (e.g.,
methotrexate, azathioprine and/or corticosteroids). This example
describes using methods of the prevent invention to determine the
disease activity profiles of samples from a series of clinical
trials.
[0476] In the analysis, we investigated the relationship between
antidrug antibodies to inflixamab (ATI) and IFX concentrations in
the cohort. It was determined that 90.6% of the patients were ATI+0
(58/64), when ATI+ samples were defined to be those with total
ATI>3.13 U/ml at at least one time point. The median
concentration of IFX in ATI positive samples was 0 .mu.g/ml and
3.74 .mu.g/ml in ATI negative samples (P<2.2 10-.sup.16 by Mann
Whitney U Test). The concentration of neutralizing antibodies was 0
in ATI+ samples. The results suggest that the presence of ATI
reduces IFX concentration in a patient on IFX therapy. The range of
IFX concentration for ATI- samples was 0.0-67.28 .mu.g/ml. In ATI+
samples the IFX concentration was 0.0-26.15 .mu.g/ml. In ATI+
samples with neutralizing antibodies (Nab) the IFX concentration
ranged from 0-1.07 .mu.g/ml. FIG. 10A shows that correlation
between IFX concentration and the presence of ATI in samples of
clinical study #2A. The results also demonstrated that the odds of
being ATI positive versus ATI negative are significantly less for
samples treated with an immunosuppressant agent (ISA, e.g.,
methotrexate, azathioprine, corticosteroids, and combinations
thereof). In this analysis 814 samples were evaluated. The odds of
being ATI+ was significantly less for ISA-treated samples than of
being ATI- (odd ratio=0.564; p<0.00001 by Fisher's Exact Test).
In addition, fewer ISA treated samples expressed neutralizing ATIs.
Of the 34 ATI+ samples with neutralizing antibodies analyzed, 9 of
the 34 samples were ISA-treated and 25 samples were non-ISA treated
samples. This indicates that ISA therapy can reduce the progression
to ATI, and even neutralizing antibodies to IFX. FIG. 10B
illustrates the relationship between ISA therapy and the presence
of ATI in the study.
[0477] Next, we investigated the relationship between ATI and
inflammatory markers. As described herein, total ATI BLOQ was set
at 0. CRP concentration was determined by methods such as a CEER
assay. The results show that the median concentration of CRP was
lowest (5.0 .mu.g/ml) in ATI- samples and higher (10.0 .mu.g/ml) in
ATI+ samples. Sample expressing neutralizing ATI had a yet higher
median concentration of CRP (10.0 .mu.g/ml). All pair-wise
comparisons between CRP concentrations and ATI status should that
the values were significantly different (p<0.0001 by Mann
Whitney U tests). FIG. 10C illustrates the relationship between CRP
concentrations and the presence of ATI (ATI and/or neutralizing
ATI).
[0478] We also investigated the relationship between ATI and loss
of response to therapy. In the cohort, samples were marked as
having a "response", "loss of response" and "no information"
regarding IFX therapy. The samples were further categorized as
being "True" if having a loss of response or "False" if not having
a loss of response. In total 777 samples were analyzed. The results
showed that in samples marked as "True", there was a significantly
higher odds ratio of also being ATI positive (odds ratio=2.254,
p<0.0001 by Fisher's Exact Test). Surprisingly, more samples
that were positive for neutralizing antibodies to IFX were
determined to be responsive to IFX, as compared to being no longer
responsive. Of 34 neutralizing ATI+ samples, 21 were marked as
"response" and 8 were marked as "loss of response". FIG. 10D
illustrates the relationship between loss of responsiveness to IFX
therapy and the presence of ATI in the study. FIG. 11 illustrates
that levels of ATI and neutralizing antibodies can be determined
over time in a series of samples from various patients
[0479] We compared the concentration of IFX to the presence of the
inflammatory marker CRP. We defined "IFX presence" per sample as
"True" if IFX was >=0.1 .mu.g/ml which is the LOD of the assay.
The results suggest that the median CRP concentration was not
different between samples with IFX present or without IFX present.
The median CRP level was 7.40 .mu.g/ml in samples with IFX, while
median CRP=7.55 .mu.g/ml in samples with IFX absent (p=0.591 by
Mann Whitney U Test). FIG. 12A illustrates the comparison of CRP
levels to the presence of IFX.
[0480] We also compared the relationship between infusion reaction
to the presence of ATI. The analysis included a total of 797
samples; 30 samples were categorized as having infusion reaction
("Yes") and 767 samples were categorized as having no infusion
reaction ("No"). 29 samples that had an infusion reaction were also
ATI+ (odds ratio=35.54, p<0.0001 by Fisher's Exact Test). FIG.
12B illustrates the relationship between the presence of ATI and
the infusion reaction. Patients expressing ATI were more likely to
have had an infusion reaction. Yet, for the 27 samples with
neutralizing ATI, no infusion reaction was observed in 22 samples.
The remaining 5 samples with neutralizing ATI had infusion
reaction.
[0481] B. Clinical Study #2B
[0482] In this analysis of clinical study #2B, we investigated the
relationships between the presence of ATI, IFX concentration,
administration of ISA, the expression of inflammatory markers
(e.g., CRP), and loss of response to IFX treatment. We determined
that the median IFX concentration was higher in samples expressing
ATI compared to those not expressing the antidrug antibodies. 15.2%
of the patients (16 out of 105) were ATI+ with a total ATI>3.13
U/ml at at least one time point. Of the 489 samples analyzed, the
median IFX concentrations were 0.59 .mu.g/ml in ATI+ samples and
7.78 .mu.g/ml in ATI- samples (p<2.2.times.10.sup.-16 by Mann
Whitney U Test). FIG. 12C illustrates the relationship between IFX
concentration and the presence of ATI in the cohort. The analysis
showed that there are high odds of developing antibodies to IFX
when immunosuppressants have been withdrawn (odds ratio=0.412,
p=0.0367 by Fisher's Exact Test). FIG. 12D illustrates the
correlation between the presence of ATI and the withdrawal of ISA
therapy at a specific, given date. We determined that ATI positive
samples have a higher median concentration of CRP (9.6 .mu.g/ml,
p=1.25.times.10.sup.-12 by Mann Whitney U Test), compared to ATI
negative samples (median CRP=1.5 .mu.g/ml). FIG. 13A illustrates
the relationship between ATI and the inflammatory marker CRP. Our
analysis showed that the odds of experiencing a loss of response to
IFX was higher in patients determined to be ATI positive at any
time point. (odds ratio=3.967, p=0.0374 for Fisher's Exact Test).
FIG. 13B illustrates the correlation between the presence of ATI at
any time point and responsiveness to IFX treatment. Loss of
response to IFX was also correlated to a higher median
concentration of the inflammatory marker CRP. In the analysis there
were 14 samples with loss of response at follow-up and 91 samples
from responders. The median CRP levels were 11.767 .mu.g/ml for
those with loss of response and 2.585 .mu.g/ml for those with
response. Patients who had lost response to IFX had a significantly
higher mean CRP (p=7.45.times.10.sup.-5 by Mann Whitney U Test).
FIG. 13C shows that loss of response can be related to an increase
in CRP. CRP was also significantly higher in samples lacking
detectable IFX 2. Samples were determined to have IFX ("IFX
present") if the level of IFX was >=to 0.1 .mu.g/ml per sample
(e.g., LOD of the assay). The median CRP was 1.6 .mu.g/ml in IFX
present samples and 13 .mu.g/ml in IFX absent samples
(p=3.69.times.10.sup.-5 by Mann Whitney U Test). FIG. 13D
illustrates the association between the presence of IFX and CRP
levels. In this study "ATI+" was defined as a sample with total
ATI>3.13 U/ml at at least one time point.
[0483] C. Clinical Study #2C
[0484] In this analysis of clinical study #2C, we investigated the
relationship between IFX levels and the presence of ATI. It was
determined that ATI+ have a significantly lower median IFX of 0.43
.mu.g/ml as compared to ATI- samples which have a median IFX of
3.28 .mu.g/ml (p=1.95.times.10.sup.-4 by Mann Whitney U test). FIG.
14A shows that lower IFX levels are associated with the presence of
ATI.
[0485] As such, in one embodiment, the present invention provides a
method for determining whether an individual is a candidate for
combination therapy wherein said individual is administered
infliximab, the method comprising:measuring for the presence or
absence of ATI in said individual; and administering an
immunosuppressant (e.g., MTX) is the individual has significant
levels of ATI. In certain aspects, the concentration level of CRP
is indicative of the presence of ATI.
Example 8
Disease Activity Profiling for TNF-.alpha. Mediated Disease
Prognosis Using Patient Samples from Clinical Study #3
[0486] This example illustrates using methods of the present
invention to monitor the therapeutic efficacy of anti-TNF drug
therapy. In particular, pooled data including study data,
pharmacokinetics data, follow-up study data of clinical study #3
were analyzed. The results showed that the median IFX concentration
of 0.0 .mu.g/ml was lower in ATI positive samples compared to an
IFX concentration of 12.21 .mu.g/ml ATI negative samples
(P<2.2.times.10-16 by Mann Whitney U test). FIG. 14B shows that
lower IFX levels are associated with the presence of ATI in these
clinical samples. FIG. 14C illustrates that the same correlation
between IFX levels and ATI was also present in the study data,
follow-up study and in the pharmacokinetics study (p<0.05 by
Mann Whitney U tests). We also used methods of the present
invention to determine that a high concentration of ATI in a sample
have a neutralizing effect on IFX. In particular, high
concentrations of ATI act as neutralizing antibodies to inflixamab.
Samples with a high concentration of ATI had an IFX level of 0
.mu.g/ml. FIG. 15A illustrates the relationship between ATI levels
including neutralizing ATI and IFX.
Example 9
Methods of Disease Activity Profiling Including the PRO
Inflammatory Index in Patients Receiving Humira
[0487] This example illustrates methods of the present invention
including determining the level of TNF-.alpha. biologic (e.g.,
adalimumab (Humira); ADL) and the presence of anti-drug antibodies
to the TNF-.alpha. biologic (e.g., ATA) in a patient sample. In
this analysis, one sample represents one patient and a total of 98
CD samples were evaluated. 2.04% (2 out of 98 CD patients) of the
samples were positive for ATA., when ATA positivity was set as
total ATA>0. Surprisingly, the two ATA positive samples also had
the highest concentrations of ADL. FIG. 15B illustrates an
association between ADL concentration and the presence of ATA in
patient samples.
[0488] This example describes an exemplary PRO Inflammatory Index
(PII). The example also illustrates the use of the PII in patient
samples receiving Humira (adalimumab) and different drug
combinations. FIG. 16A describes the details of an exemplary PRO
Inflammatory Index. The PII can represent a single per-sample score
describing inflammation levels based on five biomarkers. The score
is obtained from the logarithmic transformation of the sum of the
five biomarkers. In some embodiments, the biomarkers include VEGF
in pg/ml, CRP in ng/ml, SAA in ng/ml, ICAM in ng/ml and VCAM in
ng/ml. FIG. 16B illustrates that there is no obvious relationship
between the PII and the concentration of ADL in an array of samples
with ADL alone or in combination with other drugs. This could be
due to the appearance of high ADL trough serum concentration in the
sample cohort. These is a significant negative correlation between
PII and ADL concentration (p=1.66.times.10.sup.-5 and Spearman's
Rho=-0.459). A similar negative correlation relationship was found
between IFX and PII.
[0489] We also compared the relationship between the PII and the
presence of therapeutic agents used to treat TNF-.alpha. mediated
diseases. ADL positive samples were defined as samples with an ADL
concentration of greater than 0 .mu.g/ml. The results showed that a
higher PII was detected in patients on Humira compared to patients
on Remicade and Humira. FIG. 17 shows a plot of the PII scores for
patients receiving Humira and Humira in combination with other
drugs such as Remicade, Cimzia, Asathioprine and Methotrexate.
[0490] As such, in one embodiment, the present invention provides a
method for monitoring Crohn's disease activity, the method
comprising:
[0491] determining an inflammatory index comprising the measurement
of a panel of markers comprising VEGF in pg/ml, CRP in ng/ml, SAA
in ng/ml, ICAM in ng/ml and VCAM in ng/ml;
[0492] comparing the index to an efficacy scale or index to monitor
and manage the disease.
Example 10
Methods for Improved Patient Management
[0493] This example describes methods for improved patient
management to assist in developing personalized patient
treatment.
[0494] In some embodiments, patients with active CD and UC can be
analyzed using a mobility shift assay (see, e.g., PCT Publication
No. WO 2011/056590, the disclosure of which is hereby incorporated
by reference in its entirety for all purposes) in conjunction with
disease activity profiling. FIG. 18 shows details of the methods of
the present invention for improving the management of patients with
CD and/or UC. In some embodiments, the methods of disease activity
profiling comprise pharmacokinetics, and determining the presence
and/or levels of disease activity profile markers and/or mucosal
healing markers.
[0495] In some embodiments, disease activity profiling comprises
methods of detecting, measuring, and determining the presence
and/or levels of biomarkers, cytokines, and/or growth factors.
Non-limiting examples of cytokines that can be used in disease
activity profiling include bFGF, TNF-.alpha., IL-10, IL-12p70,
IL-1.beta., IL-2, IL-6, GM-CSF, IL-13, IFN-.gamma., TGF-.beta.1,
TGF-.beta.2, TGF-.beta.3, and combinations thereof. Non-limiting
examples of inflammatory markers include SAA, CRP, ICAM, VCAM, and
combinations thereof. Non-limiting examples of anti-inflammatory
markers include TGF-.beta., IL-10, and combinations thereof.
Non-limiting examples of growth factors include amphiregulin
(AREG), epiregulin (EREG), heparin binding epidermal growth factor
(HB-EGF), hepatocye growth factor (HGF), heregulin-.beta.1 (HRG)
and isoforms, neuregulins (NRG1, NRG2, NRG3, NRG4), betacellulin
(BTC), epidermal growth factor (EGF), insulin growth factor-1
(IGF-1), transforming growth factor (TGF), platelet-derived growth
factor (PDGF), vascular endothelial growth factor (VEGF), stem cell
factor (SCF), platelet derived growth factor (PDGF), soluble
fms-like tyrosine kinase 1 (sFlt1), placenta growth factor (PIGF),
fibroblast growth factors (FGFs), and combinations thereof.
[0496] In other embodiments, disease activity profiling comprises
detecting, measuring and determining pharmacokinetics and mucosal
healing. In some aspects, mucosal healing can be assessed by the
presence and/or level of selected biomarkers and/or endoscopy. In
some instances, mucosal healing can be defined as the absence of
friability, blood, erosions and ulcers in all visualized segments
of gut mucosa. In some embodiments, biomarkers of mucosal healing,
include, but are not limited to, AREG, EREG, HG-EGF, HGF, NRG1,
NRG2, NRG3, NRG4, BTC, EGF, IGF-1, HRG, FGF1, FGF2 (bFGF), FGF7,
FGF9, SCF, PDGF, TWEAK, GM-CSF, TNF-.alpha., IL-12p70, IL-1.beta.,
Il-2, IL-6, IL-10, IL-13, IFN-.gamma., TGF-.alpha., TGF-.beta.1,
TGF-.beta.2, TGF-.beta.3, SAA, CRP, ICAM, VCAM, and combinations
thereof. In some embodiments, a growth factor index can be
established using statistical analyses of the detected levels of
biomarkers of mucosal healing. In some instances, the growth factor
index can be associated with other markers of disease activity, and
utilized in methods of the present invention to personalize patient
treatment.
[0497] FIG. 19 shows the effect of the TNF-.alpha. pathway and
related pathways on different cell types, cellular mechanisms and
disease (e.g., Crohn's Disease (CD), rheumatoid arthritis (RA) and
Psoriasis (Ps)). FIG. 20 illustrates a schematic of an exemplary
CEER multiplex growth factor array. In particular embodiments, the
methods of the present invention can employ this array. As
non-limiting examples, FIG. 21A-F illustrate multiplexed growth
factor profiling of patient samples using this array. In
particular, longitudinal analysis of growth factors, such as AREG,
EREG, HB-EGF, HGF, HRG. BTC, EGF, IGF, TGF.alpha., and VEGF, was
performed on a collection of patient samples. FIGS. 21B and E
illustrate the determination of the level of serological and immune
markers, such as ASCA-a, ASCA-g, Cbir1 and OmpC, in samples from
Patient 10109, Patient 10118 and Patient 10308. FIG. 21G shows the
exemplary growth factor arrays performed on samples from healthy
controls, patients with IBS-C, and patients with IBS-D.
[0498] A series of multiplexed CEER growth factor and CRP arrays
was performed on patient samples. Tables A-D (below) highlight
longitudinal analysis of mucosal healing in patient samples. The
following Table (A) shows that CRP and growth factors can be
predictive of mucosal healing:
TABLE-US-00010 Subject Collection TGF TGF ID Date CRP EGF bFGF VEGF
FGF1 Tweak beta1 beta2 10101 Collection 1 3.98 315.67 4.83 1454.94
15.16 0.65 68.64 964.02 10101 Collection 2 0.13 N 365.74 P 3.79 N
1201.53 N 15.37 P 6.39 P 78.52 P 562.77 N 10103 Collection 1 0.66
439.03 4.00 969.78 17.86 35.05 67.68 300.36 10103 Collection 2
15.44 P 372.64 N 3.90 N 881.27 N 17.00 N 35.50 P 73.85 P 311.76 P
10109 Collection 1 15.86 418.89 1.66 223.85 13.52 6.35 63.79 386.64
10109 Collection 2 1.22 N 162.75 N 0.49 N 177.42 N 15.66 P 5.69 N
57.93 N 544.34 P 10118 Collection 1 0.41 126.86 1.31 1173.42 13.11
9.51 71.43 339.43 10118 Collection 2 3.54 P 282.16 P 3.03 P 1200.74
P 14.43 P 1.92 N 68.69 N 920.00 P 10308 Collection 1 1.80 336.45
2.23 1361.03 15.05 5.35 98.94 730.52 10308 Collection 2 155.95 P
525.57 P 23.83 P 3233.27 P 15.34 P 11.18 P 153.21 P 466.49 N 10503
Collection 1 2.10 237.62 6.76 760.17 13.63 13.07 64.72 475.00 10503
Collection 2 27.39 P 215.81 N 3.59 N 1135.46 P 11.81 N 61.50 P
90.11 P 737.82 P 11003 Collection 1 6.32 1.58 408.49 14.06 7.69
40.53 395.19 11003 Collection 2 0.16 N 123.57 1.90 P 394.88 N 14.81
P 5.12 N 35.85 N 221.67 N 11601 Collection 1 0.23 241.76 3.36
173.02 15.43 2.54 46.92 589.91 11601 Collection 2 0.92 P 310.64 P
6.89 P 169.40 N 17.31 P 12.50 P 61.67 P 514.21 N 11602 Collection 1
1.71 327.92 15.31 562.30 12.82 12.13 58.15 1120.06 11602 Collection
2 1.93 P 338.88 P 4.83 N 334.69 N 12.36 N 14.61 P 59.83 P 1599.43 P
12121 Collection 1 6.85 484.22 4.89 477.49 11.90 25.90 35.44
1307.92 12121 Collection 2 2.16 N 607.95 P 4.72 N 842.54 P 11.13 N
10.93 N 43.32 P 1284.24 N 12121 Collection 3 58.64 P 458.80 N 0.81
N 286.72 N 12.38 P 6.71 N 60.23 P 631.34 N 190 Collection 1 0.74
353.47 252.71 1.63 22.79 190 Collection 2 25.18 P 941.21 P 656.11 P
4.71 P 84.07 P 492 Collection 1 0.66 351.79 3.61 20.02 492
Collection 2 91.49 P 962.96 P 27.89 P 2546 Collection 1 4.73 857.25
866.87 10.37 31.01 2546 Collection 2 28.18 P 805.11 N 826.44 N 7.23
N 56.28 P "N" and "P" denote a negative or positive relationship
between pairs of observations for each marker, respectively per
subject. Underlined data are number pairs above upper limit of
quantitation and are assumed to have a positive relationship.
[0499] The following Table B lists CRP and growth factors
predictive of mucosal healing:
TABLE-US-00011 Subject Collection HB TGF ID Date CRP AREG HGF HRG
EGF BTC alpha 10101 Collection 1 3.98 12.16 26.60 143.10 6.60 0.00
3.10 10101 Collection 2 0.13 N 9.91 N 23.90 N 46.50 N 5.00 N 0.00
2.00 N 10103 Collection 1 0.66 26.06 41.30 2000.00 17.80 0.00 6.60
10103 Collection 2 15.44 P 50.00 P 82.70 P 2000.00 P 18.10 P 0.00
6.60 P 10109 Collection 1 15.86 13.81 27.40 243.10 7.80 0.00 3.30
10109 Collection 2 1.22 N 9.71 N 0.00 N 95.60 N 6.30 N 0.00 2.60 N
10118 Collection 1 0.41 27.10 26.12 541.30 10.51 0.00 7.07 10118
Collection 2 3.54 P 21.40 N 29.95 P 492.70 P 7.83 N 26.00 P 6.42 P
10308 Collection 1 1.80 0.00 45.10 0.00 4.28 0.00 0.00 10308
Collection 2 155.95 P 0.00 121.67 P 0.00 5.31 P 0.00 0.00 10503
Collection 1 2.10 4.90 35.36 0.00 2.80 0.00 0.00 10503 Collection 2
27.39 P 7.10 P 46.07 P 145.70 P 5.80 P 0.00 1.70 P 11003 Collection
1 6.32 10.20 31.11 205.20 5.80 0.00 3.30 11003 Collection 2 0.16 N
7.30 N 25.98 N 124.50 N 5.40 N 0.00 2.80 N 11601 Collection 1 0.23
0.00 8.10 0.00 8.40 8.00 1.39 11601 Collection 2 0.92 P 6.00 P
12.90 P 467.00 P 8.80 P 8.30 P 1.91 P 11602 Collection 1 1.71 0.00
55.50 0.00 7.50 7.40 0.70 11602 Collection 2 1.93 P 0.00 11.90 N
0.00 5.40 N 7.70 P 0.88 P 12121 Collection 1 6.85 0.00 37.40 0.00
7.80 9.00 2.63 12121 Collection 2 2.16 N 0.00 54.00 P 0.00 8.00 P
7.40 N 0.24 N 12121 Collection 3 58.64 P 0.00 66.30 P 0.00 7.90 P
7.50 N 1.86 N 190 Collection 1 0.74 0.00 10.80 0.00 5.90 0.00 1.90
190 Collection 2 25.18 P 0.00 11.50 P 0.00 5.70 N 0.00 0.00 N 492
Collection 1 0.66 10.90 13.00 441.80 19.40 0.00 3.11 492 Collection
2 91.49 P 8.60 N 21.00 P 355.60 P 13.30 N 0.00 2.12 N 2546
Collection 1 4.73 11.55 16.70 299.40 8.00 0.00 3.50 2546 Collection
2 28.18 P 26.12 P 38.80 P 912.70 P 13.30 P 0.00 5.40 P "N" and "P"
denote a negative or positive relationship between pairs of
observations for each marker, respectively per subject. Underlined
data are number pairs above upper limit of quantitation and are
assumed to have a positive relationship.
[0500] The following Table C shows that CRP and growth factors can
be predictive of mucosal healing:
TABLE-US-00012 Subject Collection TGF ID Date CRP EGF VEGF Tweak
beta1 2834 Collection 1 6.88 604.22 624.03 2.00 68.05 2834
Collection 2 24.33 P 631.31 P 509.73 N 3.72 P 44.79 N 3570
Collection 1 105.46 1046.04 191.49 5.51 33.61 3570 Collection 2
1.31 N 487.25 N 237.91 P 6.33 P 41.29 P 3713 Collection 1 7.76
1117.85 1267.74 3.94 45.08 3713 Collection 2 107.22 P 633.56 N
957.18 N 5.44 P 39.59 N 5301 Collection 1 7.62 32.19 5301
Collection 2 36.61 P 217.02 389.33 2.88 30.89 N 7757 Collection 1
4.49 838.39 11.24 7.90 43.35 7757 Collection 2 138.56 P 705.18 N
5.33 N 7966 Collection 1 3.03 120.82 326.72 5.59 38.67 7966
Collection 2 31.04 P 1089.52 P 691.29 P 6.81 P 48.68 P 8075
Collection 1 6.81 968.26 840.06 8.10 58.65 8075 Collection 2 34.62
P 620.97 N 876.55 P 6.27 N 51.36 N 8127 Collection 1 34.41 323.51
310.67 5.54 41.13 8127 Collection 2 2.78 N 318.02 N 284.46 N 6.87 P
51.87 P 8431 Collection 1 4.53 1829.91 214.78 2.18 52.82 8431
Collection 2 30.51 P 816.10 N 301.14 P 3.47 P 58.41 P 3831
Collection 1 32.95 804.87 491.46 6.83 36.16 3831 Collection 2 0.29
N 491.17 N 912.29 P 7.31 P 23.62 N 3852 Collection 1 68.59 494.06
252.18 6.10 32.76 3852 Collection 2 1.00 N 291.49 N 122.66 N 6.56 P
39.22 P 3852 Collection 3 0.60 N 375.97 N 100.53 N 1.34 N 22.83 N
5477 Collection 1 23.17 550.58 485.76 7.51 36.73 5477 Collection 2
2.12 N 1101.83 P 575.69 P 7.55 P 34.98 N 7456 Collection 1 35.21
51.23 452.45 6.13 22.05 7456 Collection 2 0.89 N 496.87 P 366.73 N
0.99 N 14.19 N "N" and "P" denote a negative or positive
relationship between pairs of observations for each marker,
respectively per subject. Underlined data are number pairs above
upper limit of quantitation and are assumed to have a positive
relationship.
[0501] The following Table D shows that CRP and growth factors can
be predictive of mucosal healing:
TABLE-US-00013 Subject Collection HB TGF ID Date CRP AREG HGF HRG
EGF BTC alpha 2834 Collection 1 6.88 2834 Collection 2 24.33 P 3570
Collection 1 105.46 3570 Collection 2 1.31 N 3713 Collection 1 7.76
0.00 17.40 0.00 6.90 0.00 2.20 3713 Collection 2 107.22 P 0.00
13.20 N 0.00 5.80 N 0.00 0.00 N 5301 Collection 1 7.62 5301
Collection 2 36.61 P 7757 Collection 1 4.49 2.60 26.00 0.00 6.90
0.00 6.82 7757 Collection 2 138.56 P 2.70 P 43.00 P 0.00 5.70 N
0.00 6.82 P 7966 Collection 1 3.03 4.20 21.00 0.00 6.10 34.58 3.33
7966 Collection 2 31.04 P 2.40 N 36.00 P 0.00 5.70 N 0.00 N 2.62 N
8075 Collection 1 6.81 8.50 14.70 359.30 8.90 0.00 0.00 8075
Collection 2 34.62 P 6.40 N 16.80 P 0.00 N 6.30 N 0.00 0.00 8127
Collection 1 34.41 13.20 16.50 476.30 23.00 16.90 5.28 8127
Collection 2 2.78 N 9.40 N 16.90 P 355.30 P 9.10 N 0.00 N 3.46 N
8431 Collection 1 4.53 5.00 21.40 0.00 5.50 0.00 0.00 8431
Collection 2 30.51 P 31.30 P 24.80 P 671.90 P 23.10 P 0.00 25.00 P
3831 Collection 1 32.95 3831 Collection 2 0.29 N 3852 Collection 1
68.59 0.00 17.08 0.00 6.00 0.00 0.00 3852 Collection 2 1.00 N 0.00
13.84 N 0.00 5.10 N 0.00 0.00 3852 Collection 3 0.60 N 5477
Collection 1 23.17 2.30 15.25 143.20 5.60 0.00 0.00 5477 Collection
2 2.12 N 2.00 N 19.05 P 0.00 N 5.40 N 0.00 2.50 P 7456 Collection 1
35.21 16.70 0.00 315.30 8.10 0.00 5.10 7456 Collection 2 0.89 N
6.60 N 12.92 P 128.90 N 3.60 N 0.00 2.70 N "N" and "P" denote a
negative or positive relationship between pairs of observations for
each marker, respectively per subject. Underlined data are number
pairs above upper limit of quantitation and are assumed to have a
positive relationship.
[0502] Tables A, B, C and D show marker values and relationships
between pairs of observations in CRP and growth factor data. Using
a criterion of .alpha.=0.1, we identified an association between
three growth factors and CRP. The following Table (E) shows a
two-by-two contingency table that highlights that an increase or
decrease in AREG, HRG and TGF was found to be significantly
associated with an increase or decrease of CRP:
TABLE-US-00014 AREG* HRG** TGF-alpha*** Positive Negative Positive
Negative Positive Negative CRP Positive 6 4 7 1 8 5 Negative 0 6 1
5 1 6 *denotes p = 0.034. **denotes p = 0.026. ***denotes p =
0.07.
[0503] FIG. 22 illustrates the association between CRP levels and
the growth factor index score in determining disease remission.
[0504] Further studies for identifying predictive markers of
mucosal healing may include samples from several clinical studies.
As one non-limiting example, Clinical Study A may include 413
samples (paired samples with 1-5 samples per patient). Clinical
data may detail patient age, sex, weight, date of diagnosis,
disease location, sample collection dates, dose, colonoscopy,
improvement of mucosa, presence of mucosal healing, and/or
concomitant medication useage. In Clinical Study A, colonoscopy may
be performed prior to first drug infusion. As another non-limiting
example, in Clinical Study B, 212 UC samples may be analyzed (110
samples were diagnosed for CD at follow-up and 102 samples were
diagnosed for UC based on mucosal healing). Clinical data may
detail patient age, sex, weight, date of diagnosis, disease
location, sample collection dates, IFX dose, colonoscopy results
(endoscopic activity score), albumin level, CRP level, and/or Mayo
score. In Clinical Studies A and B, three infusions may occur at
week 0, 2 and 6 during induction. 6 additional drug infusions may
be performed during the maintenance phase at week 14, 22, 30, 38,
46 and 52. A second colonoscopy may be performed during the
maintenance phase. A third colonscopy may be performed during
follow-up and patients may continue treatment if responsive to
drug.
[0505] The methods of the present invention can be used to create
personalized therapeutic management of a TNF.alpha.-mediated
disease. A personalized therapeutic regimen for a patient diagnosed
with IBD can be selected based on predictors of disease status
and/or long-term outcome as described herein, including, but not
limited to, a Crohn's prognostic test (see, e.g., PCT Publication
No. WO 2010/120814, the disclosure of which is hereby incorporated
by reference in its entirety for all purposes), a disease activity
profile (e.g., disease burden), a mucosal status index, and/or a
PRO Inflammatory Index as described in Example 5. Using the methods
of the present invention, it can be determined that a patient has
mild disease activity and the clinician can recommend, prescribe,
and/or administer a nutrition-based therapy (FIG. 23A). Yet, if it
is determined that a patient has mild disease activity with an
aggressive phenotype, a nutrition-based therapy in addition to
thiopurines can be recommended, prescribed, and/or administered. A
similar therapy can be recommended, prescribed, and/or administered
if it is determined that the patient has moderate disease activity
(FIG. 23B). If it is determined that a patient has moderate disease
activity with an aggressive phenotype, either a combination of
thiopurines and nutrition therapy (Nx) or an appropriate anti-TNF
drug can be recommended, prescribed, and/or administered. In some
instances, an anti-TNF monitoring test (see, e.g., PCT Publication
No. WO 2011/056590, the disclosure of which is hereby incorporated
by reference in its entirety for all purposes) can be used to
determine if the patient is likely to respond to the therapy. In
the case when severe disease activity is determined, an appropriate
anti-TNF drug administered at an optimized dose can be recommended
and/or prescribed (FIG. 23C). In such instances, an anti-TNF
monitoring test (see, e.g., PCT Publication No. WO 2011/056590, the
disclosure of which is hereby incorporated by reference in its
entirety for all purposes) can be used to predict if the patient is
likely to be responsive to drug. In other instances, it can be
recommended and/or prescribed that a patient having severe disease
activity also receive nutrition-based therapy.
[0506] In some embodiments, the methods of the present invention
can be used in a treatment paradigm to personalize patient
treatment (FIG. 24). First, treatment can be selected based on the
expression of mucosal status markers. Next, drug dose can be
selected based on disease burden (e.g., disease activity index).
After the therapeutic drug is administered, the initial response
can be determined from the expression of markers of mucosal
healing. ATM monitioring can be used to identify patient who are
responsive or non-responsive to therapy. Non-responsive patients
can then be prescribed an appropriate anti-TNF drug.
Example 11
Novel Infliximab (IFX) and Antibody-to-Infliximab (ATI) Assays are
Predictive of Disease Activity in Patients with Crohn's disease
(CD)
[0507] Previous studies indicate that patients with CD who have a
higher trough concentration of IFX during maintenance dosing are
more likely to benefit from treatment. However, development of ATIs
can result in increased drug clearance and loss of response.
Therapeutic drug monitoring may allow clinicians to maintain
effective drug concentrations. Although previous ATI assays have
been limited by the inability to measure ATIs in the presence of
drug, fluid-phase IFX and ATI assays have overcome this problem
(see, e.g., PCT Publication No. WO 2011/056590, the disclosure of
which is hereby incorporated by reference in its entirety for all
purposes). We used these assays to evaluate the relationship
between serum IFX concentration, ATIs and disease activity.
[0508] Methods: 2021 serum samples from 532 participants in 4
prospective CD RCTs or cohort studies (COMMIT, Leuven dose
optimization study, Canadian Multicenter and IMEDEXl) that
evaluated the maintenance phase of IFX treatment were used, and
data were combined for analysis. IFX and ATI serum levels were
measured using a HPLC-based fluid phase assay. CRP, measured by
ELISA, was used to assess disease activity. ROC analysis determined
the IFX threshold that best discriminated disease activity, as
measured by CRP. We examined pairs of samples taken over sequential
time points and evaluated the relationship between IFX and ATI
presence in the pair's first data point and CRP in the subsequent
measurement. There were 1205 such observations. We identified four
distinct patient groups, namely IFX.gtoreq.threshold and ATI-,
IFX<threshold and ATI-, IFX.gtoreq.threshold and ATI+, and
IFX<threshold and ATI+. Regression analyses assessed the
potential interaction between IFX and ATI as predictors of CRP.
[0509] Results: CRP can best differentiate IFX status with an IFX
concentration threshold of 3 .mu.g/ml (ROC AUC=74%). Using paired
sequential samples both ATI and IFX were associated with median CRP
(Table 2). Although ATI+ patients had higher CRP levels overall,
within this group there was no association between IFX higher than
threshold and subsequent CRP. In ATI- patients, CRP was
significantly higher in patients with IFX levels <3 .mu.g/ml. In
the regression analysis ATI positivity, IFX.gtoreq.3 .mu.g/ml and
the interaction term were all significant predictors of CRP. CRP
was 31% higher in ATI positive patients than those who were ATI
negative and 62% lower in patients with IFX levels.gtoreq.3
.mu.g/ml compared to those with IFX<3 .mu.g/ml.
[0510] Conclusions: We have shown that ATI positivity is predictive
of increased disease activity, while an IFX concentration above the
threshold value of 3 .mu.g/ml is predictive of significantly lower
disease activity. In ATI+ patients, IFX concentrations above 3
.mu.g/ml had no effect on CRP, indicating that the benefits of IFX
are diminished in the presence of ATI despite the presence of
optimal drug concentration. These findings support the concept that
therapeutic drug monitoring is an important tool in optimizing IFX
therapy. Using paired sequential samples and regression analysis,
both ATI and IFX were associated with median CRP as shown in the
following table:
TABLE-US-00015 Median CRP Concentration (ng/ml; interquartile
range) In ATI- Patients In ATI+ Patients Significance IFX <3
.mu.g/ml 5.65 (1.68, 16.1) 8.40 (3.10, 20.1) *** IFX .gtoreq.3
.mu.g/ml 1.50 (1.00, 4.70) 9.90 (5.82, 20.2) ** Significance *** NS
Median CRP concentrations and interquartile ranges (in parentheses)
in ng/ml. Asterisks denote significance levels of two-sample
Mann-Whitney U tests (***, p < 0.001; **, p < 0.01; *, p <
0.05; NS, not significant).
Example 12
Novel Infliximab (IFX) and Antibody-to-Infliximab (ATI) Assays are
Predictive of Disease Activity in Patients with Crohn's disease
(CD)
[0511] This example illustrates the use of infliximab (IFX) and
antibody-to-infliximab (ATI) assay in predicting disease activity
in patients with Crohn's disease (CD). This example also
illustrates a method of determining the threshold of IFX that can
best discriminate disease activity as measured by C-reactive
protein (CRP) levels. This example also illustrates the association
of both ATI and IFX to CD and CRP levels, which can serve as a
measure of disease activity.
[0512] Previous studies have indicated that patients with CD who
have a higher trough concentration of IFX during maintenance dosing
are more likely to benefit from treatment. However, development of
ATIs can result in increased drug clearance and loss of response.
Therapeutic drug monitoring may allow clinicians to maintain
effective drug concentrations. Although previous ATI assays have
been limited by the inability to measure ATIs in the presence of
drug, the fluid-phase IFX and ATI assays described in PCT
Publication No. WO 2011/056590 (the disclosure of which is hereby
incorporated by reference in its entirety for all purposes) have
overcome this problem.
[0513] In this study we used fluid-phase IFX and ATI assays to
evaluate the relationship between serum IFX concentration, ATIs and
disease activity, as measured by CRP. We analyzed 2,021 serum
samples from 532 participants in 4 prospective CD randomized
controlled trials (RCTs) or cohort studies, including COMMIT,
Leuven dose optimization study, Canadian Multicenter and IMEDEXl.
The combined analysis was restricted to samples during maintenance
of IFX treatment. There was evidence of non-heterogeneity among
pooled CRP.
[0514] IFX and ATI serum levels were measured using a HPLC-based
fluid phase assay. CRP was measured by ELISA and used to assess
disease activity. Receiver-operator curve (ROC) analysis was
performed to determine the IFX trough threshold (e.g., amount or
concentration) that can best discriminate disease activity (e.g.,
between high and low CRP values). FIG. 25 shows the ROC analysis.
CRP and nine IFX trough thresholds were analyzed and the ROC area
under receiver-operator characteristic curve (AUC) are as
follows:
TABLE-US-00016 IFX trough threshold (.mu.g/ml) 0.1 1.0 2.0 3.0 4.0
5.0 6.0 7.0 8.0 ROC 0.682 0.727 0.733 0.743 0.727 0.717 0.699 0.689
0.678 AUC
[0515] The ROC analysis showed that CRP can best differentiate IFX
status with an IFX concentration threshold of 3 .mu.g/ml (ROC
AUC=74%). For example, at an IFX through concentration threshold of
3.0 .mu.g/ml, a randomly chosen sample with a "low" IFX serum
concentration will have a higher CRP level than a randomly chosen
sample with a "high" IFX serum concentration 74.3% of the time. In
the IFX, ATI and CRP association analysis, a serum IFX trough
threshold of 3.0 .mu.g/ml was used.
[0516] To determine the association of serum IFX concentration,
ATI, and CRP levels over time, we examined pairs of samples taken
over sequential time points. A 100-day time gap limit was imposed
for the time points. We evaluated the relationship between the
presence of IFX and ATI in the pair's first data point and CRP in
the subsequent measurements (FIG. 26A). FIG. 26B shows CRP levels,
IFX serum concentration and ATI status at sequential time points
for a sample. In total, 1,205 observations were examined.
[0517] Regression analysis (e.g., ordinary least squares
regression) was performed to assess the potential interaction
between prior IFX and prior ATI as predictors of disease (i.e., CRP
levels). In particular, CRP was log transformed at the second time
point observation. Prior IFX is the first time point with IFX
concentration above or below the calculated trough threshold of 3
.mu.g/ml. Prior ATI is the first time point ATI is above or below
3.13 U/ml which is the limit of detection (LOD). Using paired
sequential samples and regression analysis, both ATI and IFX were
associated with median CRP as shown in the following table:
TABLE-US-00017 Median CRP Concentration (ng/ml; interquartile
range) In ATI- Patients In ATI+ Patients Significance IFX <3
.mu.g/ml 5.65 (1.68, 16.1) 8.40 (3.10, 20.1) *** IFX .gtoreq.3
.mu.g/ml 1.50 (1.00, 4.70) 9.90 (5.82, 20.2) ** Significance *** NS
Median CRP concentrations and interquartile ranges (in parentheses)
in ng/ml. Asterisks denote significance levels of two-sample
Mann-Whitney U tests (***, p < 0.001; **, p < 0.01; *, p <
0.05; NS, not significant).
[0518] The results shows that the factors and interactions between
the factors are significant. The regression coefficients were
calculated to be 0.272 for ATI+ samples and -0.979 for IFX.gtoreq.3
.mu.g/ml.
[0519] We identified four distinct patient groups: (1)
IFX.gtoreq.threshold and ATI-, (2) IFX<threshold and ATI-, (3)
IFX.gtoreq.threshold and ATI+, and (4) IFX<threshold and ATI+.
Of the 1,205 observations used in the analysis, 605 were
IFX.gtoreq.threshold and ATI-; 196 were IFX<threshold and ATI-;
41 were IFX.gtoreq.threshold and ATI+; and 363 were
IFX<threshold and ATI+.
[0520] Although ATI+ patients had higher CRP levels overall, within
this group there was no association between IFX levels higher than
threshold and CRP (FIG. 27). In ATI- patients, CRP levels were
significantly higher in patients with IFX levels less than
threshold (FIG. 27).
[0521] In the regression analysis, ATI positivity, IFX.gtoreq.3
.mu.g/ml and their interaction were all significant predictors of
CRP levels. CRP was 31% higher in ATI+ patients than those who were
ATI-, and 62% lower in patients with IFX levels.gtoreq.3 .mu.g/ml
compared to those with IFX<3 .mu.g/ml. The relationship between
IFX concentration and CRP levels differs between ATI+ and ATI-
patient groups.
[0522] In this study we showed that ATI positivity is predictive of
increased disease activity, as measured by CRP. We also showed that
IFX concentration above the threshold value of 3 .mu.g/ml is
predictive of significantly lower disease activity. In ATI+
patients, IFX concentrations above 3 .mu.g/ml had no effect on CRP
levels, suggesting that the benefits of IFX are diminished in the
presence of ATI even despite the presence of optimal drug
concentration.
[0523] We showed that disease activity, as measured by CRP, is
strongly linked to both IFX and ATI in a large combined dataset.
Thus, patients with active Crohn's disease can benefit from
knowledge of both IFX and ATI levels at trough. Based on the
experimental derivation of these relationships, the following
treatment paradigms were created. For instance, a symptomatic
patient with Crohn's disease with IFX<threshold at trough and
ATI- can benefit from an increased dose of IFX therapy. A patient
with IFX.gtoreq.threshold and ATI- can benefit from receiving
endoscopy or switching therapy. A symptomatic patient with
IFX<threshold at trough and ATI+ can benefit from switching
therapy if ATI is high or optimizing therapy dose if ATI is low. A
patient with IFX.gtoreq.threshold and ATI+ can benefit from
switching therapy if disease activity (e.g., CRP level) is high.
Alternatively, if disease activity (e.g., CRP level) is low in that
patient, further monitoring is recommended. The treatment paradigms
are described in the following table:
TABLE-US-00018 ATI- ATI+ IFX < threshold Increase dose Switch
therapy (high ATI) or Optimize dose (low ATI) IFX .gtoreq.
threshold Check endoscopy Switch therapy (high activity) or or
Switch therapy Monitor (low activity)
[0524] These findings demonstrate that therapeutic drug monitoring
using methods of the present invention are important tools in
optimizing IFX therapy.
[0525] Although the foregoing invention has been described in some
detail by way of illustration and example for purposes of clarity
of understanding, one of skill in the art will appreciate that
certain changes and modifications may be practiced within the scope
of the appended claims. In addition, each reference provided herein
is incorporated by reference in its entirety to the same extent as
if each reference was individually incorporated by reference.
* * * * *
References